A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification

Lung cancer is the primary reason of cancer deaths worldwide, and the percentage of death rate is increasing step by step. There are chances of recovering from lung cancer by detecting it early. In any case, because the number of radiologists is limited and they have been working overtime, the increase in image data makes it hard for them to evaluate the images accurately. As a result, many researchers have come up with automated ways to predict the growth of cancer cells using medical imaging methods in a quick and accurate way. Previously, a lot of work was done on computer-aided detection (CADe) and computer-aided diagnosis (CADx) in computed tomography (CT) scan, magnetic resonance imaging (MRI), and X-ray with the goal of effective detection and segmentation of pulmonary nodule, as well as classifying nodules as malignant or benign. But still, no complete comprehensive review that includes all aspects of lung cancer has been done. In this paper, every aspect of lung cancer is discussed in detail, including datasets, image preprocessing, segmentation methods, optimal feature extraction and selection methods, evaluation measurement matrices, and classifiers. Finally, the study looks into several lung cancer-related issues with possible solutions.


Introduction
Lung cancer is a signifcant obstacle to the survival of humans, and many people lose their lives every year because of lung cancer. Early detection of pulmonary nodules is essential for improving lung cancer patients' survival rates. Nodules are abnormal tissue growths that can occur anywhere in the body. Tey can also grow in in-depth skin tissues as well as internal organs. When a nodule forms in the lungs, it is referred to as a pulmonary nodule. A nodule with a diameter of three centimeters or less is called a tumor [1]. Tere are mainly two kinds of tumors. It can be either malignant or benign. Malignant tumors mean cancerous tumors. It can grow and spread all over the body. On the other hand, benign tumors are not cancerous. Tey either do not spread or grow very slowly or do so. Tey usually do not return after being removed by a physician. Approximately 95% of lung nodules are benign [2]. But it can be malignant also. A larger lung nodule, such as 30 millimeters or more in diameter, has a higher risk of being cancerous than a smaller lung nodule [3].
Lung cancers are broadly divided into non-small-cell lung cancer (NSCLC) and small-cell lung cancer (SCLC) [4]. About 80%-85% of lung cancers are NSCLC, and 10%-15% of all lung cancers are SCLC. Te survival rate of lung cancer is low. In 2008, there were 12.7 million cancer cases and 7.6 million cancer deaths, with 56% of patients and 64% of fatalities occurring in economically developing countries. Lung cancer is the most common cancer site in men, accounting for 17% of all new cancer cases and 23% of cancer deaths [5]. Lung cancer is diagnosed at an advanced stage in approximately 70% of patients, with a 5-year survival rate of approximately 16%. However, if lung cancer is detected early, it has a better chance of being treated successfully, with a 5-year survival rate of 70% [6,7]. One of the leading causes of lung cancer is smoking. It can even happen to those who have never smoked. It can be increased by exposure to secondhand smoking, arsenic, asbestos, radioactive dust, or radon.
Several attempts have been made since 1980 to develop a system that can detect, segment [8,9], and diagnose pulmonary nodules from CT scans [10]. Te detection of pulmonary nodules is complicated because their appearance varies depending on their type, whether they are malignant, and their size, internal structure, and location. Segmentation has become a big problem, and it now requires a lot of diferent methods to solve it. Each technique focuses on another part of the problem [11]. Tese systems are referred to as computer-aided diagnosis systems (CAD). Tey go beyond simple image processing to provide specifc information about the lesion that can aid radiologists in making a diagnosis. Te idea of CAD was initially presented in 1966 [12]. Researchers frst thought about using computers to make automated diagnoses. Tere were no other ideas or technologies at the time, so CAD technology was still in its infancy until the 1980s when the concept moved from automatic computer diagnosis to CAD [13]. Te relevant ideas and computer technology were also quickly evolving at the time. All of these factors contributed to the advancement of CAD technologies. Te frst study on lung cancer CAD systems based on CT scans was published in 1991 [14]. Several competitions, such as Lung Nodule Analysis 2016 (LUNA16) [15] and Kaggle Data Science Bowl (KDSB) [16], have attracted several professional teams who have created lung cancer CAD algorithms in recent years. By making it easier to compare alternative algorithms, these competitions have aided in advancing lung cancer CAD technology. Lung cancer CAD can detect lung nodules and predict the likelihood of malignancy, making it a handy tool for doctors. Computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems are two types of CAD systems. Te former can detect and locate pulmonary nodules, while the latter can classify them as benign or malignant.
Several researchers analyzed the existing articles previously for detecting and diagnosing lung nodules using CT images. Yang et al. [17] examined the use of deep learning techniques to detect and diagnose lung nodules in particular. Convolutional neural networks (CNNs) have been the most widely used deep learning methods in treating pulmonary nodules. CNNs have produced excellent results in lung cancer CAD systems. In the 2017 DSB competition, for example, the winning team's algorithm was a CNN model [18], and a CNN model developed by Google and published in Nature outperformed six professional radiologists [19]. Te problem of pulmonary nodule application has been tackled using various deep learning methods. Poap et al. [20] introduced a heuristic and nature-inspired method for X-ray image segmentation-based detection over aggregated images. Te proposed approach for automating medical exams delivers favorable results for detecting diseased and healthy tissues. A heuristic red fox heuristic optimization algorithm (RFOA) was also presented for medical image segmentation by Jaszcz et al. [21]. In addition, the operation of heuristics was modifed for the analysis of two-dimensional images, with an emphasis on equation modifcation and the development of a unique ftness function. Kumar et al. [22] were the frst to employ an autoencoder (AE) to diferentiate benign from malignant pulmonary nodules, while Chen et al. [23] were the frst to use a deep belief network (DBN) in the context of pulmonary nodule CAD. To improve training efciency, Wang and Chakraborty [24] proposed a sliced recurrent neural network (RNN) model. In their method, multiple layers of the RNN were taught simultaneously, which reduced training time. To train a deep learning model, a large amount of data is required. However, few labeled datasets are available for researchers due to the need for specialists and the time-consuming nature of the efort. A generative adversarial network (GAN) is based on the negative training paradigm and uses training to generate new images that are comparable to the original, which has piqued the interest of many medical imaging researchers [25]. Some researchers have chosen to generate lung nodule images with a GAN to increase the amount of data available [26]. Lung cancer detection has become more structured, making it more usable and reliable. Tis structure provides a basic workfow diagram for detecting lung cancer. However, the structure is not always the same, and there may be variations. When it comes to lung cancer detection, the process is divided into several steps, including collecting images or datasets, preprocessing the images, segmentation, feature extraction, feature selection and classifcation, and receiving the results. Figure 1 depicts the method for detecting cancer in images.
(i) Dataset. Dataset collection is the initial step to starting the process. Tere are mainly 3 types of image datasets used for lung cancer detection: computed tomography (CT) scans, magnetic resonance imaging (MRI), and X-rays. CT scan images are mainly used because of their high sensitivity and low cost. Also, it is more available rather than MRI and X-ray. More about the dataset is discussed in Section 3.
(ii) Preprocessing. Image preprocessing is used to improve the original image's quality and interpretability. Te primary goal of CT image preprocessing is to remove noise, artifacts, and other irrelevant information from raw images, improve image quality, and detect relevant information. Section 5 has a brief discussion about it.
(iii) Segmentation. Te segmentation of CT images is an important step in detecting lung nodules and recognizing lung cancer. Pulmonary segmentation's main goal is to separate the pulmonary parenchyma from other tissues and organs accurately. It uses preprocessed medical images to calculate the volume of lung parenchyma. Section 6 discusses a variety of segmentation algorithms.
(iv) Feature Extraction. Te features of the segmented lung images are extracted and analyzed in this step. Feature extraction is a process in which a large amount of raw data is divided and reduced to more manageable groups after being initially collected. It makes the process a lot less complicated. Feature extraction methods are described in Section 7. (v) Feature Selection. Feature selection identifes and isolates the most consistent, non-redundant, and relevant features in model construction. Feature selection is primarily used to improve predictive model performance while lowering modeling computational costs. It is also a way to make the classifcation result more accurate. Section 8 describes the most commonly used feature selection methods. (vi) Classifcation. Classifcation is dividing a given set of data into groups of similar characteristics. It separates benign and malignant nodules based on the feature that has been selected. Well-known classifcation methods are discussed in Section 9. (vii) Result. Finally, the detection result of lung cancer shows us where the cancerous cell is in the lung. It is discussed in Section 10. Figure 2 addresses the taxonomy of this survey. Te lung nodule and cancer analysis were separated into two artifcial intelligence plans applied in clinical imaging. Tis clinical imaging was divided into seven categories. We chose studies from various eras based on their popularity to conduct this survey. We upheld a systematic review methodology in this study, which will aid future researchers in determining the general skeleton of an artifcial intelligence-based lung nodule and cancer analysis. Tis survey gives a reasonable perspective on ML and DL structures occupied with distinguishing lung cancer. Tis concentration likewise addresses the identifcation and characterization of lung nodules and malignant growth using imaging strategies. Finally, this survey coordinates a few open exploration challenges and opportunities for future scientists. We agree that this review serves as an essential guide for researchers who need to work with clinical image characterization using artifcial intelligence-based lung nodules and cancer analysis while using various clinical images. Table 1 shows a correlation between the existing surveys and our survey. Table 2 provides a summary of recent surveys and reviews that have been conducted on various approaches for the detection, segmentation, and classifcation of lung cancer.
Te survey discusses the fndings of various related research work areas like nodule classifcation, nodule identifcation, lung cancer detection, lung cancer verifcation, and so on. While looking at the present challenges, this study generates suggestions and recommendations for further research works. Te total contributions of the research are as follows: (i) Te article gives an intelligible review of detecting systems of lung nodules and cancer. (ii) Te article inspects lung nodule and cancerdetecting procedures depending on the existing systems, datasets, image preprocessing, segmentation, feature extraction, and selection techniques. Further, the paper exploits the benefts and limitations of those systems. (iii) Te article gives the procedures to detect lung nodules and cancer in a well-organized way. (iv) Finally, the survey adapts the present challenges of lung nodules and cancer detection systems, with further research on pathological diagnosis.
After going through this division, one should adapt how to get started with this topic.
Te remaining sections of the paper are organized as follows. Te methodology of the survey is described in Section 2. Various categorized datasets obtainable publicly are displayed in Section 3. Imaging modalities are briefy described in Section 4. Section 5 describes the preprocessing algorithm of the image dataset of lung cancer and nodules. Section 6 discusses the segmentation process and algorithms. Section 7 discusses the most commonly used algorithms for extracting features from CT scans, X-rays, and MRI images. Section 8 discusses the most commonly used methods for feature selection. Section 9 discusses the wellknown classifcation and detection algorithms. A comprehensive exploration of the performance for lung cancer and nodule detection is discussed in Section 10. Te challenges faced most commonly while detecting lung nodules and cancer are explained with their possible solutions in Section 11. Lastly, the conclusion of this article is given in Section 12.

Inclusion and Exclusion
Criteria. Te most important information for this survey is collected using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), which is shown in Figure 3. Table 3 shows the criteria that PRISMA uses to choose which studies to include and which ones to leave out. In addition, this table shows how to select a paper based on certain criteria and standards, which criteria are used, and whether the article is initially accepted or rejected.

Publication
Year and Type. At the start of this project, 610 papers were gathered from diferent sources, and 423 were chosen for the survey. More than 90% of these articles were published between 2010 and 2021. Terefore, we used more recent articles to update this review. Te data and the unbalanced nature of it are the current limitations [37] Discussing the most recent developments in the feld Te size of the target object within the image makes it difcult to implement a CNN; as the size of the target object varies, studies proposed training the model with images of varying scales to teach the model about this size variation [38] Providing an accurate diagnosis and prognosis is essential in lung cancer treatment selection and planning Incorporating knowledge from clinical and biological studies into deep learning methods and utilizing and integrating multiple medical imaging methods [27] Algorithms used for each processing step are presented for some of the most current state-of-the-art CAD systems Limitation of more interactive systems that allow for better use of automated methods in CT scan analysis [33] An overview of the current state-of-the-art deep learning-aided lung cancer detection methods, as well as their key concepts and focus areas Limited datasets and high correlation of errors in handling large image sizes [35] A summary of existing CAD approaches for preprocessing, lung segmentation, false positive reduction, lung nodule detection, segmentation, classifcation, and retrieval using deep learning on CT scan data Defcient data annotation, overftting, lack of interpretability, and uncertainty quantifcation (UQ) [39] A survey of what CADe schemes are used to detect pulmonary nodules will help radiologists make better diagnoses Slight increase in lung density and micronodules whose diameters are less than 3 mm are difcult to detect. For multimodality, clinical records and medical images are not combined.

Dataset
Tere are many frequently used datasets that researchers use for lung cancer diagnosis. From Table 4, it can be seen that the CT scan is currently the most reliable method for gathering data on nodule detection in lung cancer. X-rays and MRIs are also used to detect lung cancer and nodules. CT scan is used because it is a confned method that can handle most datasets well. CT scans provide a comprehensive approach for storing data for various reasons. First, the information must be procured and put away by some members or patients. It is unacceptable to have the same storing plan for diferent patients to get data. After the patients are prepared, individuals have to lie down on a table and go through a passage-like machine that will catch and gauge data. For some time, this data collection strategy has been in place, with a specifc recording period dictated by the work's motivation. Te data saved in these sessions and recordings are primarily lung nodule images estimated by blocks established in CT scans, X-rays, or MRI. CT scans, X-rays, or MRIs difer from one member to the next and from one session to the next. In this segment, the datasets are portrayed, just as the subjects and X-ray cylinder, indicators, and sessions.

Imaging Modalities
Imaging is vital for the analysis and treatment of lung nodules and cancer. Hence, this research exhibits that lung cancer analysis relies upon seven particular classifcations of clinical imaging modalities. CT scan, Xray, MRI, ultrasound (US), positron emission tomography (PET) scan, and singlephoton emission computed tomography (SPECT) are the seven clinical imaging modalities, and their combination is known as multimodalities. Te CT scan is the most basic and widely used imaging modality in lung imaging. As per Table 4, most of the work was done in computed tomography (CT) scan images. Te second-highest number of studies delivered is for X-ray images and MRI [103][104][105][106]. Another imaging technique known as a chest radiograph is an expensive method with limited accessibility. Tese should be the reasons for the lower adaptivity of chest radiographs in research, as this imaging strategy was used in a small number of examinations [107,108]. Ultrasound (US) and PET scan imaging strategies were utilized distinctly in a couple of studies [109][110][111]. Te SPECT imaging strategy has acquired prevalence as of late in lung nodule classifcation and malignant growth recognition. Because the thermogram dataset is not publicly available, a couple of studies used this imaging strategy [112]. Unfortunately, none of the researchers used histopathology. Te well-known imaging strategies are depicted in greater detail in the following section.

X-Ray.
A type of high-energy radiation, like electromagnetic waves, is called X-ray. An X-ray is also called X-radiation. X-ray imaging makes images of the inside of the human body. It shows the parts of the body in diferent shades of black and white [113]. Te soft tissues in the human body, such as blood, skin, and muscle, absorb the majority of the X-ray and allow it to transit, resulting in dark gray areas on the flm. However, a bone or tumor, which is thicker than soft tissue, prevents most X-rays from passing through and appears white on the flm [104]. Gavelli and Giampalma [114] used X-ray images to detect lung cancer. Tey calculate the sensitivity and specifcity for evaluating the outcome.

CT Scan.
A computed tomography (CT) scan is a medical imaging method utilized in radiology to get comprehensive body images for diagnostic purposes. It merges a series of X-rays taken from various viewpoints around the body and makes cuts on the bones, veins, and delicate tissues inside the body [115]. CT scans point out a cross section of the body part like bones, organs, and soft tissues more clearly than standard X-rays because normal X-rays are done in two directions. It depicts the structure, size, and location of a tumor [116]. CT scans are more detailed than standard X-rays in identifying cross sections of body parts such as bones, organs, and delicate tissues [117]. In 2018, Makaju et al. [51] used CT scan images to detect lung cancer. Using their proposed model, they attempted to achieve 100% accuracy. Zheng et al. [118] also used CT images to detect lung cancer and infammatory pseudo-tumor.

Magnetic Resonance Imaging (MRI). MRI is a clinical
imaging technique that utilizes radiofrequency signals to create point-by-point images of the organs and tissues in the body. MRI scanners use solid magnetic felds, magnetic feld gradients, and radio waves to generate images of the organs in the body [119]. MRI creates images of soft tissues in the human body that are often difcult to see with other imaging techniques. As a result, it is highly efective at detecting and locating cancers. It also generates images that allow specialists to see the location of a lung tumor and estimate its shape and size. A specifc dye named a contrast medium is applied to create a better image before the scan [106]. Cervino et al. [120] tried to track lung tumors by performing ANN in MRI sagittal images. Te mean error was 7.2 mm using only TM and 1.7 mm when the surrogate was combined with TM.

Positron Emission Tomography (PET) Scan.
PET scan is a helpful imaging method that uses radioactive substances known as radiotracers to envision and measure changes in metabolic cycles and other physiological activities, including circulation system, regional compound course of action, and absorption [121]. In addition, PET scan is a diagnostic tool that helps doctors detect cancer in the body. Te scan employs a unique shading technique that includes radioactive tracers. Depending on which part of the body is being examined, these tracers are either swallowed, ingested, or implanted into a vein in the arm [122]. Te PET scan utilizes a mildly radioactive medication to appear in spaces of the body where cells are more dynamic than regular cells. It is used to assist with diagnosing a few conditions, including malignant growth [123]. It can also help determine where the cancer has spread and whether or not it has spread. Because malignant growth cells have a higher metabolic rate than normal cells, they appear as bright spots on PET scans. Lung cancer is the bright spot in the chest that can be seen best on PET and PET-CT images [124]. Weder et al. [111] tried their model in PET scan and got a positive predictive value of 96%.

Single-Photon Emission Computed Tomography (SPECT).
SPECT is an atomic medication tomographic imaging method utilizing gamma rays. It is similar to traditional nuclear medicine planar imaging with a gamma camera, but it can provide accurate 3D data. However, it can provide accurate 3D data [125]. A SPECT scan is a test that shows how bloodstreams connect to tissues and organs [126]. Antibodies (proteins that recognize and adhere to cancer cells) can be linked to radioactive substances. First, assuming a tumor is available, the antibodies will be attached to it. Ten, at that point, a SPECT output should be possible to recognize the radioactive substance and uncover where the cancer is found [127].

Multiple Modalities.
Multiple modalities are considered an educational approach used to relieve the stress of researchers [128]. It entails giving various introductions and experiences of the substance to utilize multiple senses and abilities in a single example. Numerous modalities frequently cater to diferent learning styles [129]. Modalities can be performed using a combination of chemotherapy and radiation therapy. Concurrent chemoradiotherapy is the simultaneous administration of chemotherapy and radiation therapy [130]. Farjah et al. [131] implemented single, double, and tri-modality in their research. Tey conducted a CT scan for single modality, CT scan or PET scan with invasive staging for bi-modality, CT scan, PET Scan, and invasive stage for tri-modality. Te advantages and disadvantages of these image modalities are described in Table 5.

Image Preprocessing
Image preprocessing organizes images before they are used in model preparation and induction. Te goal of preprocessing is to improve the quality of the image so that it can be investigated more thoroughly [132]. It includes, but is not limited to, rectifcations for resizing, arranging, and shading [133]. As a result, in some cases, a change that could be an expansion may be better served as a preprocessing step in others.

Histogram Equalization.
Tere are two diferent ways to contemplate and carry out histogram leveling, either as picture change or as range change [134]. Much of the time range change is preferable because it protects the initial data [135]. It is employed in image analysis. To produce a high contrast image, the gray level intensities are expanded along the x-axis [136]. Asuntha and Srinivasan [137] used a histogram evening out to close the gap. Shakeel et al. [90] changed diferences in their dataset. Ausawalaithong et al. [98] preprocessed their picture dataset with histogram balance. It enhances the CT scan's contrast; it spreads out the most frequent pixel intensity values or stretches out the intensity range of the scan. Let I be a given CT scan image represented as a I x by I y matrix of integer pixel intensities ranging from 0 to 256. Let N denote the normalized histogram bin of image I for available intensity.
number of pixels with available intensity n total number of pixels , where n � 0, 1, . . . , 255.

Median Filter Mask.
Te median flter is a non-straight computerized separating strategy, regularly used to eliminate roughness from an image or sign [138]. Tis type of noise reduction is a common prehandling step used to work on the afterefects of later preparation. Te median flter is a sifting procedure used to remove noise from images and signals [139]. Te median flter is essential in image processing because it protects edges during clamor expulsion. It is broadly utilized as it is best at eliminating commotion while safeguarding borders [140]. Tun and Soe [141] used and claimed the median flter mask to be the best flter for their research. Shakeel et al. [142] and Ausawalaithong et al. [98] used a median flter mask in preprocessing their dataset. Asuntha and Srinivasan [137] reshaped and resized their data with a median flter. Sangamithraa and Govindaraju [143] used the median flter mask in image preprocessing to detect lung nodules. It moves through the lung images pixel by pixel, replacing each value with the median value of neighboring pixels. It can save sensitive components in a picture while fltering noise, and it is good at eliminating "salt and pepper" type noise.

Gaussian Filter.
A Gaussian flter is a flter whose response is based on a Gaussian capacity [144]. Tis efect is widely used in design software, usually to smooth out images and reduce detail [145]. Gaussian noise, also known as Gaussian distribution, is a factual noise with a possible thickness equivalent to ordinary conveyance. Tis roughness is produced by combining irregular Gaussian capacity with image capacity [146]. Tis roughness can be eliminated by using a linear flter, as it is an ideal way of eliminating Gaussian roughness. Riquelme and Akhlouf [31], Teramoto et al. [147], and Rossetto and Zhou [148] used Gaussian flters to preprocess their image dataset. Ausawalaithong et al. [98], Hosny et al. [149], and Shakeel et al. [150] also utilized this flter to reshape their dataset and use those from detecting lung nodules. Al-Tarawneh [151] and Avanzo et al. [152] used CT scans and preprocessed them with the Gaussian flter. Asuntha et al. [153], Wang et al. [154], Sang et al. [65], and Ozdemir et al. [42] smoothed and preserved edges with the Gaussian flter. Fang [155] and Song et al. [156] applied the Gaussian flter on the LUNA16 [15] dataset to detect lung cancer. Te efect of Gaussian smoothing is to blur CT scans of the lung similar to the mean flter. Te standard deviation of the Gaussian determines the degree of smoothing. Gaussian blurring the CT image minimizes the amount of noise and reduces speckles.
(i) In 1D: (ii) In 2D: where σ is referred to as standard deviation of the distribution. Te mean of the distribution is considered as 0.

Wiener Filter.
Te Wiener flter is the MSE-ideal fxed straight flter for images corrupted by added substance clamor and obscuring. Wiener flter works when the sign and roughness measures are assumed to be fxed [157]. Sangamithraa and Govindaraju [143] removed the added substance noise while also modifying the obscuring. In terms of the mean square error, Wiener fltering is ideal.It is used to measure a perfect or arbitrary target interaction by straight time-invariant sifting of a detected noisy cycle, expecting to know fxed sign and noise spectra, and adding substance noise. [158]. It restricts the overall mean square error during backward sifting and commotion smoothing. It removes the additive noise and inverts the blurring simultaneously in lung images. It eliminates the additive noise, transforms the obscuring, and limits the general mean square error during inverse fltering and noise smoothing. Te Wiener sifting is a direct assessment of the frst picture [159].

Gabor Filter.
A Gabor flter is a straight flter used in image processing for surface analysis, which means it determines whether or not there is a specifc recurrence content in the lung images in explicit terms in a restricted district surrounding the point or region of examination [160]. It investigates whether there is a particular recurrence content. It has gotten signifcant consideration as it takes after the human visual framework. It is a neighborhood operation in which the value of any given pixel in the output lung scan is determined by applying some algorithm to the Table 5: Advantages and disadvantages of imaging modality methods.

Methods
Advantages Disadvantages X-ray Harmlessly and easily helps to analyze sickness and screen treatment It can harm cells in the body, which thus can build the danger of creating malignant growth. CT scan is better than X-ray.

CT scan
It is easy, painless, and precise. It has the capacity to picture bone, delicate tissue, and veins all simultaneously. It gives point-by-point pictures of many kinds of tissue.
It requires breath holding and radiation which is difcult for a few patients Magnetic resonance imaging (MRI) It does not include radiation and is more averse to deliver an unfavorably susceptible response that might happen when iodine-based substances are utilized for X-beams and CT checks Te time required for an MRI is longer than CT. Additionally, MRI is typically less inclined to be quickly accessible than CT.

Positron emission tomography (PET) scan
It diminishes the quantity of examining meetings a patient should go through Slow developing, less dynamic cancers may not assimilate a lot of tracer Single-photon emission computed tomography (SPECT) It tends to be seen in various planes and to isolate covering structures It has signifcant expense and less accessibility Journal of Healthcare Engineering 9 importance of the pixels in the neighborhood of the corresponding input pixel. To remove noise from the dataset, Mary and Dharma [161] used the Gabor flter.
where λ means the wavelength of the sinusoidal factor, θ represents the orientation of the normal to the parallel stripes of a Gabor function, σ is the sigma/standard deviation of the Gaussian envelope, and c is the spatial aspect ratio and specifes the ellipticity of the support of the Gabor function.

Isotropic Voxel.
Voxel is short for volume pixel, the littlest recognizable box-formed piece of a 3D picture. It could be compared to the 2D pixel [162]. Te voxel size on CBCT images is isotropic, meaning that all sides are of the same size and have a uniform goal in every direction. Te voxel technique was used by Nagao et al. [163] and Wang et al. [164] to reduce sharp noises and classify lung cancer. Tis method was also used by Quattrocchi et al. [165] to reshape their dataset to detect breast cancer and lung cancer.

Tresholding.
Tresholding is a non-linear operation that changes a grayscale image into a binary image in which the two levels are allocated to pixels that are either below or above the set threshold value. It mainly converts an image from shading or grayscale into a twofold picture [166].
Tresholding is used to convert a low-contrast lung scan to a high-contrast lung scan. Tresholding is also a very efective tool in image segmentation. Its purpose is to convert grayscale images to binary format [151]. It takes the colorful or grayscale lung scans and turns them into binary scans. It diminishes the intricacy, works on acknowledgment and grouping, and changes the pixels to simplify the picture.

Binary Inversion.
High-contrast picture reversal is a picture handling strategy where light regions are planned to dim, and dull areas are scheduled to light. A rearranged high-contrast picture can be considered an advanced negative of the frst picture. Sharma et al. [167] used binary inversion to reduce noise from image datasets.

Interpolation.
Image interpolation happens when one resizes or contorts one's image, starting with a one-pixel grid and then onto the next. Zooming refers to increasing the number of pixels in an image so that the image's details can be seen more clearly [168]. Interpolation is a well-known method for surveying dark characteristics that lie between known characteristics [169]. Interpolation is a course of deciding the obscure qualities in the middle of the realized information focus. It smooths, enlarges, or averages CT scans displayed with more pixels than that for which they have initially been reconstructed. It is used to foresee obscure qualities. It forecasts values for cubic in a raster. It is generally used to foresee the obscure qualities of any geological information, such as commotion level, precipitation, rise, and so on. Te most common way to use test points with known qualities to fgure out prices at other unknown issues is by insertion [170]. It could be used to predict dark characteristics for any geographic point data, such as height, precipitation, substance obsessions, disturbance levels, and so on [171]. Several insertion strategies have previously been reported. Te broadly utilized strategies are the nearest neighbor, bilinear, bicubic, b-splines, lanczos2, and discrete wavelet transform. Lehmann et al. [172] and Zhao et al. [173] used interpolation in their dataset to detect nodules in the lungs. Liu et al. [58] used interpolation in CT scans and cleared noise, and Cascio et al. [174] used interpolation in 3D images to reduce noise.

Synthetic Minority Oversampling Technique (SMOTE).
SMOTE is an oversampling procedure that permits us to produce manufactured examples for our minority classes [175]. It is an oversampling method that creates fabricated models for the minority class. Tis computation aids in overcoming the overftting problem caused by unpredictability in oversampling [176]. Te imbalanced arrangement has the disadvantage of having too few instances of the minority class for a model to become comfortable with the choice limit [177]. Oversampling models from the minority class are regarded as one solution to this problem [178]. It randomly chooses a minority class instance and fnds its k nearest minority class neighbors. Te fabricated occasion is then created by arbitrarily selecting one of the k nearest neighbors b and coupling a and b to frame a line segment in the component space. Te manufactured examples are made by mixing the two chosen occurrences, a and b [179]. While restructuring the information with SMOTE, Chen and Wu [180] found the risk factors. Patil et al. [181] utilized it to smooth textures and minimize noise. Wang et al. [182] employed SMOTE to remove borderlines.

Contrast Limited Adaptive Histogram Equalization (CLAHE). Contrast limited AHE (CLAHE) is a variation of
versatile histogram in which the diferentiation enhancement is restricted to diminish this issue of clamor intensifcation [183]. It is utilized further to develop hazy pictures or video ability levels. It works on little districts in images, called tiles. Te adjacent tiles are then consolidated using bilinear insertion to remove the erroneous limits [184]. CLAHE calculation difers from standard HE in that CLAHE works on small areas of the image called tiles and registers a few histograms, each comparing to a specifc segment of the image and using them to rearrange the advantages of the picture [185]. In CLAHE, the diferentiation enhancement near given pixel value is provided by the incline of the change work [186]. Punithavathy et al. [187], Bhagyarekha and Pise [188], and Wajid et al. [189] used CLAHE as image preprocessing methodology. Technically, CLAHE does this by setting a threshold. If some gray levels in the lung scan exceed the threshold, the excess is evenly distributed to all gray levels. After this processing, the lung scan will not be over-enhanced, and the problem of noise amplifcation can be reduced. Table 6 shows the pros and cons of these image preprocessing techniques.

Segmentation
Lung nodule segmentation is a crucial process designed to make the quantitative assessment of clinical criteria such as size, shape, location, density, texture, and the CAD system more manageable and more efcient [196][197][198]. However, because of their solidity, location, or texture, lung nodules such as juxta-pleural (nodules directly attached to the pleura's surface), juxta-vascular (nodules connected to vessels), and ground-glass nodules can be challenging to remove. Deep learning-based segmentation is a pixel-bypixel categorization technique used to calculate organ probability [30]. Tis method is divided into two stages: the frst is the creation of the probability map using CNN and image patches and the second is the refnement of the probability map using the general background of images and the probability map [196].

Watershed.
Watershed segmentation is a technique for segmenting watersheds that use image morphology [199]. It requires the selection of at least one marker ("seed" point) within each image object, including the background as a separate object. Te markers are picked by an operator or provided by an automatic mechanism that considers the object's application-specifc information. A morphological watershed transformation helps to grow them after marking the items [200]. After the lung image preprocessing, noise is removed, images are smooth, and features are enhanced. Watershed is used in lung segmentation to identify the various regional maxima and minima [201].

U-Net.
Te U-Net [202] architecture is the most used architecture for medical image segmentation, and it signifcantly improves process performance. Te fundamental parts of the U-Net are association of convolution layers in the contracting path and deconvolution layers in the expansive direction. It includes a contraction method for capturing anatomical structure and an asymmetrical expansion method for precise localization [28]. U-Net has enabled the segmentation process to form a spatial context at several scales despite the challenges of collecting both global and local contexts. As a result, it may be trained from end to end using only a small quantity of training data [28]. Convolution layers with rectifed linear units and max-pooling layers make up the contracting route, similar to the classic architecture of a convolutional neural network. On the other hand, the expanding method entails sampling the feature map, followed by up-convolution and convolution layers using ReLU. Because of the loss of border pixels at each convolution, the extracting path's matching feature map must be cropped and concatenated with equivalent layers in the expensive direction [53]. Te input photos and their respective masks are utilized for training the U-Net during the training phase. A lung image is supplied as input to generate the appropriate mask output during the testing phase. Te mask is then applied to the relevant image to segment the area of interest, in this case, lung parenchyma [202]. (MV-CNN). Te multiview deep convolutional neural network (MV-CNN) [203] architecture for lung nodule segmentation is a CNN-based architecture that proposes to transform lung nodule segmentation into CT voxel classifcation. Te MV-CNN comprises three branches that process voxel patches from CT images in axial, coronal, and sagittal views. To obtain the voxel label, the three branches all have identical structures, including six convolutional layers, two max-pooling layers, and one fully connected layer. In addition, a parametric rectifed linear unit (PReLU) [204] is implemented as a non-linear activation function after each convolutional layer and the frst fully connected layer, and batch normalization is used for training acceleration [205].

Central Focused Convolutional Neural Network (CF-CNN).
Te central focused convolutional neural network (CFCNN) [206] architecture includes three-dimensional and two-dimensional CT imaging views for lung nodules and cancer segmentation. It uses a CT image to extract a three-dimensional patch and a two-dimensional diferent plate patch self-contained on a single voxel as input to the CNN [207] model, which predicts whether the voxel belongs to the nodule or healthy tissue class. After feeding all voxels into this CNN model, a probability map assigns each voxel a probability of belonging to a nodule. [208] is one of the most extensively used fuzzy clustering methods. Data elements can belong to multiple clusters in fuzzy clustering, and each part has a set of membership levels associated with it. It uses a CT image to extract a three-dimensional patch and a two-dimensional diferent plate patch self-contained on a single voxel as input to the CNN [207] model, which predicts whether the voxel belongs to the nodule or healthy tissue class. After feeding all voxels into this CNN model, a probability map assigns each voxel a probability of belonging to a nodule.

Fuzzy C-Means (FCM). Te FCM algorithm
6.6. Hessian-Based Approaches. Image enhancement is performed on voxels in Hessian-based strategies to acquire the 3D Hessian matrix for each voxel and calculate the relevant eigenvalues. Tese eigenvalues are used to locate and segment lung nodules in a subsequent step. To begin, multiscale smoothing is used to reduce noise in the image and make nodule segmentation easier. Following that, the 3D Hessian matrix and associated eigenvalues are computed, and the results of each method are combined to produce the segmentation masks [209]. 6.7. SegNet + Shape Driven Level Set. SegNet [210], a deep, fully convolutional network architecture, is used for coarse segmentation because it is designed primarily for pixelwise semantic labeling. A high-level network model SegNet is a network composed of encoders and decoders. SegNet is a preconfgured segmentation solution for a variety of medical imaging applications [211,212]. A batch of lung feld images is used during the training phase to feed the deep network. Te output of CNN is used to initialize the level set function for lung nodule segmentation. Te authors [213] used shape information as the primary image feature to guide the evolving shape to the intended item border.

Faster R-CNN.
Faster R-CNN [214] is an improvement on the previous Fast R-CNN [215]. As the name implies, Faster R-CNN is much faster than Fast R-CNN due to the region proposal network (RPN). Te model comprises two parts: the RPN and the Fast R-CNN. Te input image is frst subjected to convolution and pooling operations via the basic feature extraction network to obtain the image's feature map. After that, the feature map is transmitted to the RPN network, which performs preliminary border regression and classifcation judgment on the image. As the foundation for categorizing, the candidate frame is classifed based on the background or object to be recognized. Te RPN outputs the candidate frame's position and score information, and then they are sent to the Fast R-CNN for fnal processing by the fully connected layer. Tey are the fnal regression of the frame and the specifc categorization of the object to be recognized in the fnal regression. First, ConvNet [216] is used to extract feature maps from lung pictures. Next, these are fed into RPN, which returns the candidate bounding boxes. Te ROI pooling layer is then applied to reduce the size of the candidates. Finally, the proposals are transferred to a fully linked layer to obtain the fnal lung segmentation result [217]. [80] is a compact and adaptable generic object instance segmentation system. It recognizes targets in images and provides high-quality segmentation results for each target. Mask R-CNN is divided into two sections, the frst of which is RPN. It is a new network developed by Faster R-CNN [214] that replaces the previous R-CNN's selective search approach [215], and Fast R-CNN [215] integrates all content into a single network, signifcantly improving detection speed. Te second stage features two concurrent branches, one for detection and the other for classifcation and bounding box regression. Te mask branch is used for segmentation. Te preprocessing program receives raw lung image sequences and generates 2D images before processing basic images such as coordinate transformation, slice selection, mask generation, and Table 6: Advantages and disadvantages of image preprocessing methods.

Algorithms
Advantages Disadvantages Histogram equalization [190] It is a versatile strategy to the picture and an invertible administrator. It can be recuperated and expands diferentiation of pictures.
It is not the best technique for contrast improvement and is unpredictable. It expands the contrast of foundation noise.
Median flter mask [10] It can save sharp components in a picture while fltering noise, and it is good at eliminating "salt and pepper" type noise It separates picture edges and produces false noise edges and cannot smooth medium-tailed noise dissemination Gaussian flter [191] Its Fourier change has zero recurrence. It is broadly utilized to diminish picture noise and lessen detail.
It decreases subtleties and cannot deal with "salt and pepper" noise. It sometimes makes all parts blue and obscures the objects.
Wiener flter [192] It eliminates the additive noise, transforms the obscuring, and limits the general mean square error during inverse fltering and noise smoothing It is hard to acquire ideal rebuilding for the noise, relatively delayed to apply as working in the recurrence area Gabor flter [151] It investigates whether there is a particular recurrence content. It has gotten signifcant consideration as it takes after the human visual framework.
It requires huge investments. It has a high excess of provisions.
Isotropic voxel [193] It is the fastest approach and a "precise" 3D structure block, as it copies particles and opens new reproduction procedures It is hard to fabricate complex articles utilizing voxels. It does not have numerical accuracy.
Tresholding [142] It diminishes the intricacy, works on acknowledgment and grouping, and changes the pixels to make the picture simpler Tere is no assurance that the pixels distinguished by the thresholding system are bordering Binary inversion [194] CT scans were converted into black and white to detect the nodules as binary inversion will get the dark part as black which means 1 It is not a clear form to detect nodules and it has a huge chance to miss the nodules Interpolation [195] It is used to foresee obscure qualities. It forecasts values for cubic in a raster.
It obscures the edges when the decreased proportion is less SMOTE [179] It is an oversampling procedure and is powerful to handle class awkwardness. It assists with conquering the overftting issue.
It can build the covering of classes and present extra commotion. Often it does not constrict the predisposition.
CLAHE [187] Te adjoining tiles are joined using bilinear expansion to take out incorrect representation incited bounds Any commotion that might be accessible in the picture normalization. Ten, it is used in the detection and segmentation module to detect and segment the locations and contours of expected pulmonary nodules [218].

Robust Active Shape Model (RASM).
Biomedical photos typically feature complicated objects that fuctuate signifcantly in appearance from one image to the next. It can be challenging to measure or recognize the existence of specifc structures in such photos. Te RASM [219] is trained using hand-drawn contours in training images. It employs principal component analysis (PCA) to identify critical variances in the training data, allowing the model to automatically determine whether a contour is a potentially excellent object contour [220,221]. It also includes matrices that describe the texture of lines perpendicular to the control point; these are utilized to rectify positions during the search stage. Te contour is deformed by fnding the best texture match for the control points when the RASM is created. Te movement of the control points is limited by what the RASM perceives as a "normal" object contour based on the training data in this iterative procedure. Ten, PCA determines the formation's mean appearance (intensities) and variances in the training set. For example, the outline of the lungs is approximately segmented from lung images using a robust active shape model matching technique [222].

Region Growing.
Growing a region is a bottom-up process that starts with a set of seed pixels [223]. Te goal is for each seed to establish a uniformly connected zone. Intensity indicates that the measurement is used to grow a region from a seed point and to segment it. As each unallocated nearby pixel in the area is compared, the region's size increases. To compute similarity, the diference between the intensity value of a pixel and the region's mean is used. Te pixel is assigned to the area, and the minor diference is calculated. Te operation is terminated when the intensity diference between the region means and the new pixel exceeds a predetermined threshold. Each pixel's intensity values are compared to those of its neighbors starting with the seed, and if they are within the threshold, the pixel is labeled as one [219]. Next, an image of a tumor-bearing lung is uploaded. Te growth's starting point (pixel) coordinate is established, and the base value stores the selected point's color intensity. Next, the initial pixel is stored in an array's coordinates. Te process continues until all pixels are eligible and the queue is full. Te tumor tissue refers to all pixels in the points array that create a surface. Te outermost pixels are also introduced as the tumor boundary, which is curved [224]. Table 7 shows the pros and cons of segmentation methods.

Feature Extraction
Feature extraction is a process that reduces an initial collection of raw data into more manageable groups that are easier to process [228]. It reduces the number of features in a dataset by creating new ones from existing ones. Te feature extraction strategy provides new features that directly blend with the existing elements. When compared to the frst feature esteems, the new arrangement of elements will have various qualities [229]. Te main point is that fewer features will be required to capture comparable data [230].

Type of Features.
Some features need to be extracted and selected to detect lung nodules and cancer more efciently. Tere are three kinds of features. If these features are removed, the outcome can be boosted.
7.1.1. Shape-Based Feature. Shape features are signifcant because they give an option in contrast to depicting an object, utilizing its many attributes, and diminishing how much data are put away. It is one of the most fundamental characteristics of a mass. Te irregularity of the mass's shape makes removal difcult [231]. It is classifed into two types: region-based techniques and contour-based techniques. A curve estimation method, peak point characterization, and peak line following calculation are all used. Local procedures use the entire item region for its shape highlights, while form-based techniques use data in an article. Shape highlights are classifcations of a morphological part. Figure 5 shows the shape-based features very clearly.

Texture-Based Feature.
Te texture is used to segment pictures into areas of interest and group those locales. It refers to all spatial area variations and the selection of general visual perfection or harshness of images. Te texture is defned as the spatial distribution of force levels in a given area. Tey provide invaluable information about the underlying object arrangements of action in a picture, as well as their relationship to climate [231]. Texture-based features are shown in Figure 6.

Intensity-Based
Feature. Intensity refers to how much light is emitted or the mathematical worth of a pixel. As demonstrated by image feature intensity, it frst requests insights that rely upon individual pixel esteems. Te intensity of the light varies from pixel to pixel [231]. Terefore, pixel intensity is the most easily accessible pattern recognition component. Shading is typically addressed by three or four-part intensities in a shading imaging system. Te mode, median, standard deviation, and variance of image intensity can all be used to evaluate it. Figure 7 gives a clear view of intensity-based features.

Feature Extraction Methods.
Te feature extraction strategy gives us new elements, which are considered a straight mix of the current features. Te new arrangement of features will have various qualities when contrasted with the frst feature esteems. Te fundamental point is that fewer features will be needed to catch similar data.

Radiomics.
Radiomics is a strategy that separates an enormous number of provisions from clinical pictures utilizing information portrayal measurements [232].
Radiomic highlights may reveal growth examples and qualities that the unaided eye does not recognize [233]. Te standard radiomic investigation includes the evaluation of size, shape, and textural highlights that contain useful spatial data on pixel or voxel circulation and examples [234]. Echegaray et al. [235], Vial et al. [236], and Pankaj et al. [237] used the radiomics method for feature extraction. Mahon et al. [238] used radiomic radiology to extract features.

Transfer
Learning and Fine-Tuning. It frst trains a base network on a base informational index and undertakes transfer learning. Afterward, it exchanges the learned components to a subsequent objective organization to prepare for objective informational collection and errand. It trains a model on a dataset and uses it for preparing another dataset [239]. Nishio et al. [240], Sajja et al. [159], and da Nóbrega et al. [241] used transfer learning for lung cancer.

Algorithms Advantages Disadvantages
Watershed [225] Being able to divide an image into its components Takes too long to run in order to meet the deadline, sensitivity to false edges and over-segmentation U-Net [226] Images can be segmented quickly and accurately Redundancy occurs due to patch overlap, also relatively slow MV-CNN [203] No user-interactive parameters or assumptions about the shape of nodules are needed Te loss of gradients may have an efect CF-CNN [206] Gathered sensitive information about nodules from CT imaging data Less adaptable for small nodules and cavitary nodules FCM [188] Ignored noise sensitivity limitation, successfully overcoming the PCM's clustering problem Row sum constraints must be equal to one in order to work Hessian-based approaches [209] High robustness against noise and sensitivity to small objects Performance decreases for large nodule SegNet + shape driven level set [213] Correct seed point initialization with no manual intervention in the level set Segments the lung nodule partly occluded, also takes a longer time Faster R-CNN [214] Te efciency of detection is high It could take a long time to reach convergence Mask R-CNN [218] Easy to train, generalizable to other tasks, efective, and only adds a minor overhead Low-resolution motion blur detection typically fails to pick up on objects

RASM [219]
Well suited to large shape models and parallel implementation allowing for short computation times Cannot segment areas with sharp angles and is not built to handle juxta-pleural nodules Region growing [227] Te concept is simple, multiple criteria can be selected simultaneously, and it performs well in terms of noise Computing is time-consuming. Noise or variation may result in holes or over-segmentation, making it difcult to distinguish the shading of real images.
Haarburger et al. [242], Marentakis et al. [94], Paul et al. [243], and Tan et al. [244] fne-tuned image to extract features. It takes the underlying patterns, and then a pretrained model has learned and adjusted its outputs to be more suited to your problem. It saves preparation time, does better execution of neural organizations, does not require a great deal of data, and can prompt higher exactness.

LSTM + CNN.
Te LSTM strategy has turned into a fundamental structure square of neural NLP [245]. To strongly approve of moving examples, some use them as contributions to a value-based classifcation approximate to the frst LSTM production [246]. Te CNN long shortterm memory network, or CNN LSTM for short, is LSTM engineering explicitly intended for grouping expectation issues with spatial information sources, similar to pictures or recordings. Concerning the improvement of the CNN LSTM model design for system expectations. Tekade and Rajeswari [247] used a layer of CNN LSTM for feature extraction in lung image datasets. Pictures can also be addressed with high-request statistical features processed from run-length matrices or frequent models. Statistics are basic measurements that help us for better comprehension of our pictures [248].

Standard Deviation.
Standard deviation limits the ratio of reserves or dispersions of many properties. A lowquality deviation indicates that the properties will be close to the set average as a general rule. In contrast, an elite requirement deviation suggests that the properties will cover a large area [249].
where σ is the population standard deviation, N means the size of items, S i is each value from the set, and µ is the mean of all the values.

Variance.
Variance is the inconstancy in the model expectation-how much the ML capacity can change contingent upon the given informational collection [250]. In this technique, the modifed term quantifes how far each number is from the mean and how far each unit number is from the mean [251].
where µ is the mean of all the values.

Mean.
Mean is a method for executing feature extraction. It ascertains and takes away the mean for each component. A typical practice is similar to separate this worth by the reach or standard deviation.
where σ is the population standard deviation, N is the total amount of pixel present in the segmented region, S i is each value from the set, and µ is the mean of all the values.

Fourth-Moment Kurtosis.
Te kurtosis k is characterized to be the normalized fourth focal second. Te fourth second is kurtosis, which indicates the level of focal "peakedness" or, more accurately, the "largeness" of the external tails. Kurtosis denotes whether the data have been signifcantly or lightly followed by the traditional course [252].
where σ is the population standard deviation, N is the total amount of pixel present in the segmented region, S i is each value from the set, and µ is the mean of all the values.

Tird-Moment Skewness.
Skewness is a proportion of the evenness of a circulation. It estimates the measure of likelihood in the tails [253]. Te worth is frequently compared to the kurtosis of the average conveyance, which is equal to three. If the kurtosis is more remarkable than three, the dataset has heavier tails than a typical appropriation [254].
where σ is the population standard deviation, N is the total amount of pixel present in the segmented region, S i is each value from the set, and µ is the mean of all the values.

Intensity Based Features
Contrast Uniformity Energy Entropy Homogeneity Figure 7: Intensity-based features.

Entropy.
Entropy is a substantial proportion of irregularity that can describe the surface of the info picture. In image processing, discrete entropy is a proportion of the number of pieces needed to encode picture data [255]. It distinguishes diferent communication signals by describing the signals' distribution state characteristics. It is utilized in any course of weight assurance. It is vigorous and computationally fundamental. Te higher the entropy value is, the more detailed the image will be. Entropy is a proportion of haphazardness or confusion and thus a proportion of vulnerability [256]. Hussain et al. [257] used entropy to analyze lung cancer image data.

Autoencoders.
Autoencoder is a sort of neural network that is utilized to gain profciency with a compacted portrayal of unrefned information [258]. An autoencoder is made up of an encoder and a decoder submodel [259]. Te encoder compresses the information, and the decoder attempts to reproduce the contribution from the encoder's compressed variant. Ahmed et al. [260], Z. Wang and Y. Wang [261], Z. Wang and Y. Wang [262], and Kumar et al. [22] used an autoencoder to extract the feature and classify lung nodules. Te encoder compresses the input lung scan, and the decoder attempts to recreate the input lung scan from the compressed version provided by the encoder. It can be incredible to highlight extraction, conservativeness, and speed in using backpropagation.

Wavelet.
Wavelet is a frequency-selective modulation technique [263]. Te wavelet change can assist with changing over the sign into a structure that makes it a lot simpler for our pinnacle locator work. Sometime after the frst ECG signal, the wave coefcient for each scale is plotted. Wavelet was used by Kumar et al. [22] to extract features. Souf et al. [264] attempted to detect lung cancer using a wavelet. Park et al. [265] included and extracted a large number of wavelet features. A discrete wavelet transform (DWT) decomposes a signal into sets of numbers. Every set is a period series of coefcients portraying the time development of the signal in the corresponding frequency band (DWT). DWT is an efective tool for multiresolution analysis, and it is primarily pursued in signal processing, image analysis, and various classifcation systems [266]. It is broadly used in feature extraction as it is efcient, which can be declared by seeing its previous results.

Histogram of Oriented Gradients (HOG) Features.
HOG, or histogram of oriented gradients, is a feature extractor that is frequently used to extract features from picture information [266]. Adetiba 7.2.13. AlexNet, VGG16, and VGG19. AlexNet is the name of a CNN that usually afects AI in a way that unequivocally selects some way of looking at a machine [270]. It joined ReLU initiation after each convolutional and completely associated layer. VGG16 is a CNN model that is represented in the paper by Zisserman from the University of Oxford in their survey [271]. Te model achieved 92.7% of the top-5 test accuracy on ImageNet (a dataset of fourteen million-+ images, including one thousand classes). Te most striking feature of the VGG16 is that, unlike many other hyperboundaries, it consistently empties the convolution layers and uses the same cushioning and max pool [272]. VGG19 is a 19-level deep vascular neural entity. Creating more than 1,000,000 images from the Imagine information base can save an organization's pretrained presentation. Khan et al. [273] presented a pretrained VGG19-based automated segmentation and classifcation technique for analyzing lung CT images that achieved 97.83% accuracy. Table 8 shows the pros and cons of feature extraction methods.

. Feature Selection
Feature selection refers to reducing the number of input variables required to develop a predictive model. It would be preferable to reduce the number of input variables that can lower the overall computing cost of the model and, in some cases, improve its performance [281]. Te primary advantage of feature selection is that it aids in determining the signifcance of the original feature set.

Genetic Algorithm (GA).
GA is used to identify the most relevant features for lung nodule detection. Te GA generates a binary chromosome of 4096 bits in length evaluated ofine during the CADe system's training phase.
Logic "1" indicates that this feature is relevant, and logic "0" means irrelevant. As a result, it is removed from the test phase's optimized feature vector. Te ftness function is then calculated for each of the population's chromosomes [282]. It uses an evolutionary approach to determine an efcient set from lung images. Te initial stage in feature selection is to create a population based on subsets of the possible characteristics derived through lung feature extraction. Ten, the subsets of this population are evaluated using a predictive model for the target task.

mRMR.
Te minimum redundancy maximum relevance (mRMR) [93] algorithm is a fltering approach that attempts to minimize repetition between selected characteristics while also choosing the most linked attributes with class tags. First, the method determines a collection of features from lung images that have the highest correlation with the class (output) and the lowest correlation among themselves [283]. Ten, it ranks features based on mutual information using the minimal-redundancy maximal-relevance criterion. Finally, a measure is used to eliminate redundancy between features, which is stated as follows: where I(F j ;C k ) represents the mutual correlation between feature X j and class C k , I(F j ;F i ) represents the correlation between features F i and F j , S denotes the selected feature set, and m means its size (i.e., m � |S|).

Least Absolute Shrinkage and Selection Operator (LASSO).
Te LASSO [284] is a method for modeling the relationship between one or more explanatory factors and a dependent variable by ftting a regularized least-squares model to the dependent variable. It can efciently identify signifcant characteristics related to the dependent variable from a small number of observations with many features when used for compressed sensing. For example, it uses lung data by regularizing and selecting the most signifcant features simultaneously.

Sequential Floating Forward Selection (SFFS).
Te SFFS is a bottom-up search procedure that starts with the current feature set and adds new features by applying the basic SFS procedure. Ten, if there is still room for improvement in the previous set, the worst feature in the new set is removed. It counts the number of backward steps taken after each forward step [285]. If an intermediate solution at the fundamental level cannot be improved upon, there are no backward steps. Te procedure's inverse counterpart, on the other hand, can be described similarly. Because both algorithms provide "self-controlled backtracking," it is possible to fnd practical solutions by dynamically modifying the trade-of between forwarding and backward steps. Tey Table 8: Advantages and disadvantages of feature extraction methods.

Algorithms Advantages Disadvantages
Radiomics [274] It could extricate and distinguish many provisions and component types. It has a minimal expense.
For respiratory movement, it obscures data. It has restricted data of remade pictures.
Transfer learning and fne-tuning [244] It saves preparation time, does better execution of neural organizations, does not require a great deal of data, and can prompt higher exactness Transfer learning has the issue of negative exchange. Fine-tuning can at some point befuddle to sort out subclasses.
LSTM + CNN [94] It is appropriate to separate compelling elements and group, process, and foresee time series given delays of obscure length It is inclined to overftting, and it is hard to apply as it requires 4 direct layers which require a lot of memory Standard deviation [275] It gives an exact thought of how the data are appropriated. It is detached by outrageous qualities.
It tends to be afected by anomalies, is hard to ascertain or comprehend, and works out all vulnerability as error Autoencoder [276] It can be incredible for highlight extraction, conservativeness, and speed in coding utilizing backpropagation It cannot deal with adequate preparation information, prepares some unacceptable use cases, and is excessively lossy Variance [277] It treats all deviations from the mean and assists an association with being proactive in accomplishing targets It gives added weight to anomalies, is not efectively deciphered, and does not ofer wonderful precision Fourth-moment kurtosis [50] It will be in the positive structure, and conveyance about the mean gets tighter as the mean gets bigger Te weakness is that it will not have a negative or indistinct structure Wavelet [278] It ofers a synchronous restriction on schedule and recurrence space. It is quick and can isolate the fne subtleties in a sign.
It has shift afectability, its directionality is poor, and it has absence of stage data Entropy [279] It is utilized in any course of weight assurance. It is vigorous and computationally basic.
It has restricted critical thinking part and relative disparity, contingent upon the given length and biasing Histogram of oriented gradients [267] It shows invariance to photometric changes by making a dark foundation with white molecules which sharpens the articles unmistakably Te last descriptor vector develops bigger to set more efort to extricate and to prepare utilizing a given classifer Tird-moment skewness [50] It is smarter to gauge the presentation of the speculation returns, transforming the data point of high skewness into slanted conveyance It is eccentric. Te ascent and defeat of a network are best instances of the skewness.
AlexNet, VGG16, and VGG19 [280] AlexNet has 8 layers that exceed the yield dissimilar to other enactment capacities. VGG is an incredible structure block for learning reasons.
AlexNet battles to examine all provisions accordingly delivering helpless performing models. VGGNet is agonizing to prepare and its loads itself are very huge. analyze what they require in a way that does not rely on any parameters [286]. To begin, it starts with an empty set. Ten, SFFS takes backward steps on lung images after each step as long as the objective function increases. It reduces the number of unnecessary features from lung images. 8.5. PCA. PCA is a dimensionality-reduction approach commonly used to reduce the dimensionality of data by lowering an extensive collection of variables into a smaller set of variables that retains the majority of the learning from the large set of variables [287]. In addition, smaller datasets are easier to analyze and visualize, making them more accessible. For example, it chooses characteristics from lung images based on the magnitude of their coefcients.

Weight Optimized Neural Networks with Maximum
Likelihood Boosting (WONN-MLB). Newton and Raphson's MLMR preprocessing model and the boosted weighted optimized neural network ensemble classifcation algorithms are used to develop the WONN-MLB [288]. Te additive combination approach is utilized in the WONN-MLB method to incorporate the highest relevancy with the least amount of redundancy. To achieve the goal of lung cancer detection accuracy with the least amount of time and error, an ensemble of WONN-MLB qualities is used [289]. It only overviewed the extracted features from the lung feature based on the probability.

Hybrid Intelligent Spiral Optimization-Based Generalized
Rough Set Approach (HSOGR). Te hybrid intelligent spiral optimization-based generalized rough set approach (HSOGR) [90] is used to select the features. Te spiral optimization method [290] is based on spiral phenomena and aids in the resolution of the unconstrained optimization problem when picking features. Te approach employs adequate settings such as convergence and periodic descent direction in the n-dimensional spiral model to achieve success. Te approach predicts optimization characteristics according to the exploration (global solution) and exploitation (local key) phases with the help of the parameters (good solution). Rather than using a single gradient function when selecting an optimization process, this method employs several spiral points [291], which aid in the establishment of the current optimal fact at any given time. To determine whether the selected characteristics accurately aid in detecting lung cancer, the search space must be investigated using a generalized rough set procedure. Table 9 shows the pros and cons of feature selection methods.

Classification and Detection
A classifcation algorithm is an algorithm that gauges the information included, so the yield isolates one class into positive qualities and the other into negative qualities [297]. Te classifcation methodology is a supervised learning strategy used to recognize classes of novel perceptions based on information preparation [298].
Detection is a computer innovation connected with computer vision and image processing that arranges with recognizing occasions of semantic objects of a specifc class in computerized pictures and recordings [299]. It is a computer vision strategy for fnding objects in pictures or recordings. When humans look at pictures or videos, objects can be perceived and found in minutes. Te objective of object detection is to reproduce this intelligence utilizing a computer [68]. In addition, well-informed areas of article recognition incorporate face location and passerby identifcation.

Machine Learning (ML).
Machine learning is a subordinate part of artifcial intelligence, which is comprehensively characterized as the ability of a machine to impersonate shrewd human conduct [300]. Tis implies machines that can perceive a visual scene, comprehend a text written in ordinary language, or play out an activity in the actual world [301]. In addition, machine learning calculations utilize computational techniques to "learn" data straightforwardly from information without depending on a foreordained condition as a model [302]. Table 10 describes various types of machine learning (ML) algorithms.

Deep Learning (DL).
DL is a subfeld of ML and AI that copies the path of individual achieving knowledge [313]. Deep learning uses both organized and disorganized information, like text and images, to train the models [314]. Deep learning methods are stored in a sequential pattern for complexity and abstraction, whereas established ML methods are linear [315]. Moreover, deep learning eliminates some data preprocessing techniques and can extract features automatically [316]. Several deep methods have gained tremendous results. Tey are described in Table 11.

Convolutional Neural Network (CNN).
A convolutional neural network (CNN) is a methodology under DL that is capable of taking in input images, emphasizing diferent objects from the image, and distinguishing continuously [329]. In addition, CNNs are considered a type of neural network that allows for more feature extraction from captured images [330]. CNNs are classifed into three categories: convolution, max-pooling, and activation [331]. In comparison to other classifers, a CNN requires little preprocessing. Although the flters are hand-engineered in a primitive way, CNN can learn these flters/features through adequate training [332]. Table 12 describes the usage of CNN to detect lung nodules and cancer.

Hybrid System.
A hybrid structure of CNN with LeNet and AlexNet is developed for analysis by combining the layer settings of LeNet with the parameter settings of AlexNet. It begins with the LeNet architecture, incorporates ReLU, LRN, and dropout layers into the framework, and fnally develops the Agile CNN. In addition to two fully connected layers, the proposed CNN, based on LeNet, has two convolutional layers, two pooling layers, and two fully

Algorithms Advantages Disadvantages
GA [292] Tries to avoid becoming stuck in a local optimal solution GA does not guarantee an optimal solution and has high computational cost mRMR [293] Efectively reduces the redundant features while keeping the relevant features Mutual information is incompatible with continuous data LASSO [294] Very accurate prediction, reduces overftting, and improves model interpretability In terms of independent risk factors, the regression coefcients may not be consistently interpretable SFFS [295] Reduces the number of nesting issues and unnecessary features Difcult to detect all subsets PCA [296] Selects a number of important individuals from all the feature components, reduces the dimensionality of the original samples, and improves the classifcation accuracy Only considers the linear relationships and interaction between variables at a higher level WONN-MLB [288] Integrates the maximum relevancy and minimum redundancy Has certain amount of irrelevant attributes HSOGR [90] Efectively selects optimized features Its execution is complex To describe the algorithm for false positive reduction in lung nodule computer-aided detection (CAD) CT Jindex 91.39% Automatically reduces unnecessary feature subsets to get a more discriminative feature set with promising classifcation performance All false positive reduction is not done yet Logistic regression [312] Prediction of the malignancy of lung nodules in CT scans CT Sens 94.5% Additional information based on nodule size has at best a mixed impact on classifer performance Te method's performance is such that adding nodule size information has only a mixed efect on classifer performance Te dataset was too small  To get an accurate diagnosis of the detected lung nodules CT Acc 92.20 It classifed nodules using higherorder MGRF and geometric criteria Tey did not mention any reshape or resize techniques It has good parameter efciency and is parameter light. It enhances DenseNet performance and classifcation accuracy over other approaches.
Its densely connected mechanism causes feature redundancy PN-SAMP [337] Accurately identifying the nodule areas, extracting semantic information from the detected nodules, and predicting the malignancy of the nodules CT Acc 97.58 It can predict the malignancy of lung nodules and ofer high-level semantic features and nodule location Only works on CT images Journal of Healthcare Engineering 21 connected layers. Layer C1 contains 20 feature maps for each feature map in total. Te input data for each unit are linked to a neighborhood. Terefore, a connection from the input cannot extend outside the confnes of the feature map boundary. Te frst feature map in P1 is connected to the second feature map in C1 by 22 neighborhoods. Every unit in P1 is linked to the second feature map in C1. Ten, on layer C2, there are 50 feature maps. Te other options are the same as they were for the previous layers. F1 and F2 are the fnal two layers after layer P2. In terms of neuron units, F1 and F2 have 500 and 2 neuron units, respectively. Te efect of the parameters of the kernel size, learning rate, and other aspects on the performance of the CNN model is explored by varying these parameters, and an optimized setup of the CNN model is obtained as a result [339]. Tere are various hybrid methods to detect lung cancer and nodules [340][341][342][343]. Figure 8 gives an overview of CNN's hybrid structure. In artifcial intelligence, the image is commonly convolved with a particular flter (HOG or LBP) to enhance shapes and edges. Consequently, the frst stage of CNN consists primarily of Gabor-like flters. Additionally, the scale-space method was initially designed to enhance the CNN method on which we based. We proposed a novel hybrid CNN model by incorporating standard features into CNN, considering complementary characteristics of the conventional texture method and CNN. Tis hybrid model's complex distinguishable higher-level features are made up of one-of-a-kind combinations of low-level features. Te CNN flters have this hierarchy of simple elements to complex features: the frst layer flters mostly have structures that look like Gabor. In contrast, the deep layer flters in the network have features that can be identifed as objects. In this study, we combine CNN with texture features like LBP and HOG to improve the frst layer flters, which are analogous to the human visual system's ability to decompose images into their oriented spatial frequencies. Te data input layer, convolution layer, pooling layer, entire connection layer, and output layer are typically included in the structure of a CNN network. By combining data, our hybrid CNN model aims to make the data input layer easier. In contrast, the primary objective of the training is to discover the optimal model parameters by minimizing a loss function. It has been found that HOG features and LBP features are fused with CNN in a specifc way due to the signifcant diferences in shape and texture between the benign and malignant nodules. However, CNN is believed to be able to extract lung nodules with possible distinguishing features.  [155,159,240,241,303,309,344,345]. Te basic framework of transfer learning is shown in Figure 9.

Performance Evaluation
It is hard to choose which metrics to use for various issues, and observational studies have shown yet assessed graphic elements to gauge diferent parts of the calculations [346]. It is often difcult to say which measurements are most appropriate for evaluating the analysis due to the frequent weight gain errors between the expected and actual values [347]. Te interpretation of ML estimations is reviewed depending upon critical accuracy, which is routinely improper, assuming that there ought to emerge an occurrence of unequaled information and error cost shift strikingly [175]. ML execution evaluations include a degree of compromise between the true positive and accurate negative rate and between recall and precision. Te receiver operating characteristic (ROC) curve depicts the compromise between the false negative and false positive rates for each possible cutof.  Te efectiveness of any ML model is still up in the air utilizing measures like TP rate, FP rate, TN rate, and FN rate [350]. Te sensitivity and specifcity measures are commonly used to clarify demonstrative clinical tests as well as to assess how excellent and predictable the diagnostic test is [37]. Te TP rate or positive class accuracy is the sensitivity measurement, while the TN rate or negative class accuracy refers to the specifcity measurement [351]. Tere is frequently a compromise between the four measurements in "real-world" applications.

Classifcation Measurements.
Tere are a lot of methods used for the classifcation of lung nodule and lung cancer. Te widely used metrics for classifcation problems are as follows.

Precision.
Precision is the number of relevant reports recovered by a search isolated by the number of pieces retrieved. In short, precision is the number of pieces recovered that are important. It checks how exactly the model functions by actually taking a look at the correct, true positives from the anticipated ones [249].

Recall/Sensitivity.
Recall/sensitivity is the number of pertinent records recovered by a search isolated by existing signifcant archives. Sensitivity is another name for recall. Te test's sensitivity refects the likelihood that the screening test will be positive among unhealthy people. Te number of applicable archives recovered is referred to as recall. It computes the number of true positives detected by the model and marks them as positives [352]. Finally, it estimates the capacity of a test to be positive when the condition is present. It is otherwise called false negative rate, review, Type II error, β error, error or oversight, or elective theory [69].

Accuracy.
Accuracy is the level of closeness to ground truth. For example, the accuracy of an estimation is a proportion of how close the deliberate worth is to the actual value of the amount. Te estimation accuracy might rely upon a few factors, including the breaking point or the goal of the estimating instrument [353].
where TP, TN, FP, and FN mean true positive, true negative, false positive, and false positive, respectively. Aside from this, there are other types of accuracy, such as predictive accuracy and average accuracy. Predictive accuracy should be estimated based on the diference between observed and predicted values [354,355]. Average accuracy is the average of every accuracy per class (amount of accuracy for each class anticipated/number of classes) [356].
10.2.4. F1-Score. F-Measure or F1-score combines both precision and recall into a binary measure that catches the two properties, giving each similar weighting. Te arithmetic mean of the two proportions is precision and recall [357]. Te F-measure is used to fne-tune precision and recall. It is frequently used for evaluating data recovery frameworks, such as search engines, as well as some types of ML models, particularly in natural language processing [358]. F1-score is the function of precision and recall. It is evaluated when a balance between precision and recall is needed [359].  [278].

Receiver Operating Characteristic Curve (ROC Curve) and Area under the ROC Curve (AUC).
A ROC curve is a graphical plot that outlines the symptomatic capacity of a twofold classifer framework as its separation edge is fuctuated [360]. ROC analysis provides methods for selecting ideal models and automatically removing imperfect ones from the expense setting or class conveyance. ROC analysis is directly and naturally linked to cost/beneft analysis of demonstrative dynamics [361]. ROC curves are considered a fantastic asset as an accurate display measure in location/ characterization hypothesis and speculation testing. For a variety of reasons, AUC is often preferred over accuracy [362]. Indeed, since it is probably the most widely used performance metric, it is very uncomfortable to adjust how AUC works [363] properly.

ROC Curve.
Te ROC curve addresses the performance of the proposed model at all characterization limits [364]. Te ROC curve summarizes classifer execution over a range of TP and FP error rates. It is a graph of the true positive rate versus the false positive rate (TPR versus FPR). A point on the ROC curve between (0, 100) would be ideal [365]. ROC helps investigate the compromises among various classifers over a scope of situations, which is not great for circumstances with realized error costs [366][367][368][369].
10.2.8. AUC. AUC coordinates the region under the ROC curve from (0, 0) to (1,1). It gives the total proportion of all conceivable characterization edges [370]. AUC has a range of 0 to 1. Te AUC esteem for a correctly classifed version will be 1.0, while it will be 0.0 in the case of a completely incorrect classifcation [371]. It is amazing for two reasons: frst, it is scale-invariant, which means it examines how well the model is anticipated rather than the overall qualities; and second, it is grouping limit invariant, which means it examines the model's exhibition regardless of the chosen edge [372]. Te region under the curve (AUC) is most favored because the bigger the region, the better the model. Te AUC additionally has a decent translation as the likelihood that the classifer positions an arbitrarily picked positive occasion over a haphazardly picked negative one [373]. Te AUC is a useful measurement for classifer execution because it is independent of the chosen standard and earlier probabilities [374]. AUC can be used to establish a predominance connection between classifers. If the ROC curves cross, the absolute AUC is a normal comparison between models [375][376][377][378][379][380].

Segmentation Measurements.
Tere are a lot of methods used for the segmentation of lung nodule and lung cancer. Te widely used metrics for segmentation problems are as follows.

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Journal of Healthcare Engineering 10.4. Jaccard Index. Te Jaccard index, otherwise called the Jaccard similarity coefcient, is a measurement that checks the closeness and variety of test sets. It is defned as the width of the crossing point divided by the width of the association of two name sets. It is a proportion of comparability for the two information arrangements, ranging from 0% to 100% [382]. Te higher the rate, the more comparable the two populaces.
10.5. Dice Coefcient. Te Dice similarity coefcient, otherwise called the Sorensen-Dice list or Dice coefcient, is a factual instrument that estimates the comparability between two arrangements of information [383]. Te Dice coefcient should not be more noteworthy than 1. A Dice coefcient, for the most part, goes from 0 to 1 [384]. If the coefcient result is greater than 1, the execution may need to be rechecked [385]. It was used as a measurable approval metric to evaluate the reproducibility of manual divisions as well as the spatial crossover precision of robotized probabilistic partial division of MR images, as represented on two clinical models [386][387][388]. It is a substantial proportion of the comparability rate between two example sets: where p represents predicted target values (p 1 , p 2 , . . . , p n while a represents actual value: a 1 , a 2 , ..., a n , in which n represents total number of data points.

Root Mean Square Error (RMSE)
. RMSE is the square root of the mean of the square of the entirety of the error. RMSE is a good proportion of accuracy, but it should only be used to analyze and compare prediction errors of diferent models or model setups for a single variable, not between factors because it is scale-dependent [249].

Small Nodules.
Small-nodule segmentation is critical for the early identifcation of lung cancer [395]. Tin-slice high-resolution computed tomography (HRCT) has enabled the visibility of tiny nodules less than 5 mm in diameter, which was previously invisible using previous-generation CT technology. Accurate segmentation of such small nodules is required to assess the malignancy of the lesions. A partial-volume efect is the primary technical concern when dealing with tiny nodules (PVE). Te spatial discrimination used in CT imaging allows a single voxel to represent multiple tissue types by averaging their intensity values. Tis induces PVE and picture blur, particularly near lesion margins, challenging segmentation. When dealing with smaller lesions, PVE becomes more pronounced since the fraction of mistakes over the lesion volume increases. Tis makes measuring the area/volume of tiny nodules more difcult. Te partial-volume approach (PVM) [396] is presented for calculating nodule volume based on the consistency of the average attenuation quantities. PVM outperforms other thresholding algorithms in volumetric accuracy, according to their phantom study. SPVA (segmentation-based partial-volume analysis) [397] is proposed to extend the PVM approach to include VOI segmentation into the nodule core, parenchyma area, and partial-volume region. A histogram from the partial volume region was used to estimate the volume of the nodule near its boundary. Finally, the proposed RAGF [398] yields an elliptical approximation of the lesion boundary.

Nodules
Attached to Vessels. Lung nodules are frequently connected to other pulmonary structures such as the airways, blood vessels, parenchymal walls, and diaphragm. Because the CT values of nodules and these nontarget objects are frequently extremely similar, determining the extent of the nodule from these structures becomes a difcult technical issue. Juxta-vascular nodules are nodules that connect to blood vessels. Morphological fltering is a systematic strategy for this purpose [397,[399][400][401][402][403]. Because the proportion of nodules that attach to vessels/airways is often minimal compared to the entire extent of the 3D nodule surface, basic MOs such as erosion, dilatation, and opening are frequently efective in most juxta-vascular situations [400,402]. Tese fundamental operators were combined with convex-hull operations [397,404] and 3D moment analysis [405] to refne the segmentation process after it was completed. Geometric/shape constrained segmentation is another prominent strategy in this context [398,403,[406][407][408]. Tis method incorporates shape-based prior information into the segmentation process to bias the results toward a spherical/nodular shape. It suppresses elongated nontarget components linked to the target.

Nodules Attached to Parenchymal Wall and
Diaphragm. Juxta-pleural nodules are cases that are attached to the parenchymal wall or the diaphragm. Tese nodules are connected to the chest wall and pleural surface. Many automated measurement algorithms struggle with these nodules because they need to determine where the nodule ends and the chest wall begins. Solitary nodules, on the other hand, that do not border any other structures, such as airways or blood arteries, are much easier to segment [409].

Ground-Glass Opacity Nodules.
Te ground-glass opacity (GGO) nodule is a nodule with subsolid CT values that are much lower than usual solid nodules. Tey are classifed into two types based on whether or not solid components are present: non-solid/pure and partially solid/ mixed. GGO nodule segmentation is a technological issue because it is difcult to distinguish their tiny boundaries and model their uneven appearances. In clinical practice, modern CT technology's more excellent picture resolution has enabled the investigation of small GGO nodules. Although their growth is frequently slow [410], such GGO nodules, particularly mixed ones, have been linked to a high risk of malignancy [411]. Recent clinical studies are part of the histological spectrum of peripheral adenocarcinomas, which encompass premalignant atypical adenomatous hyperplasia (AAH) and malignant bronchioloalveolar carcinoma (BAC) [412]. Over ten years, a tiny non-solid GGO representing AAH or BAC can gradually grow into an invasive lung adenocarcinoma [410]. In this method, segmentation is accomplished by labeling each voxel with a nodule/background label based on a probabilistic decision rule established from training data.

Article Selection Bias.
A measurement of association, such as a risk ratio, that is distorted as a result of sample selection that does not accurately refect the target population is known as selection bias. Te selection of individuals, groups, or data for analysis in such a way that proper randomization is not achieved, failing to ensure that the obtained sample is representative of the intended population, is known as selection bias. On the other hand, selection bias might be an issue: the sociodemographic profle of DLCST participants was better. Tey had greater psychological fortitude than the general population of people who smoked a lot [413]. As a result, selection bias could lead to underestimating the actual psychosocial efects [413]. According to a psychometric analysis of survey data and qualitative focus group interviews, abnormal and false positive LCS results can have a wide range of psychosocial efects that can be adequately quantifed with PROMs [414,415]. Te fnest articles and specifcs of each are described in Table 13.

Efcient CADe System.
Developing an efcient computer-aided detection (CADe) system for detecting lung nodules is a difcult task. Te level of automation, speed, and ability to recognize nodules of varying shapes, such as irregularly shaped nodules rather than only spherical ones, as well as the CADe system's ability to detect cavity nodules, nodules attached to the lung borders, and small nodules, are all critical considerations to consider (e.g., less than 3 mm).
11.6. Volumetric Measurements. Volumetric measurements are essential because various sizes in diferent situations make the system more accurate. When calculating the growth rate in the volumetric unit, the global movement of patients caused by their actions and the local activity of the entire lung tissue caused by respiration and heartbeat should be considered. It is impossible to distinguish between Longest survivor at end of follow-up was 61 months Te authors recommend that surgery should be advocated after ensuring that curative resection of the lung primary can be achieved 5-year follow-up Higashiyama et al. [416] (retrospective cohort study (level 4, good)) 9 patients with isolated adrenal metastases from surgically resected lung cancer (4 non-curative and 5 curative)

Survival
Adrenalectomy group: 2/5 alive at 24 and 40 months, respectively, and 3/5 died at 9, 17, and 20 months, respectively All patients in the palliative group had a disease-free interval of 7 months. Tis selection bias may explain some of the observed diference in survival in addition to the infuence of treatment strategy. 5 treated with adrenalectomy followed by adjuvant chemo or radiotherapy 4 treated with palliative chemo or radiotherapy Palliative group: all died within 6 months Te authors concluded that short FDIs are probably due to lymphatic spread and probably signify a more aggressive tumor Maximum follow-up of 40 months changes caused by the direct application of global and local registration to the segmented nodule and changes in the shape of the nodule caused by breathing and heartbeat. Te research directions that should be inspected to uplift the lung nodule and cancer detection outcomes are described here. Trough profound investigation on this topic, the recommendations for the study are described below. Table 14 represents challenges and limitations in lung nodule and cancer diagnosis, as well as research directions in terms of the dataset, architectures, and so on.
(i) Datasets focused on CT scans are available openly.
Ultrasound, PET scans, and SPECT datasets, on the other hand, are not publicly available. Furthermore, studies utilizing such imaging modalities use unpublished datasets. Tese datasets should be made public for future research and implementations. (ii) Like U-Net and SegNet, segmentation models have provided sophisticated segmentation results across various image datasets. Furthermore, implementing these techniques involving diferent modalities may improve lung nodule and cancer detection results. (iii) All kinds of nodules need to be investigated.
Implementing feature extraction and selection can detect any nodule. Te selection of features and classifers can be used to identify nodules. Te most common methods for selecting features are genetic algorithms, WONN-MLB, and HSOGR. Feature extraction, on the other hand, is critical for detecting nodules. Most of the time, radiomic methods extract features from lung images. HOG, autoencoders, and wavelets should also be investigated to be more accurate. (iv) Random forest, SVM, DBN with RM, and CNNs are primarily used for lung cancer diagnosis. ML techniques such as boosting, decision trees, and DL networks of various types such as GANs and clustering should be analyzed. CNN is widely used to detect lung nodules and cancer because it can extract essential features from images. CNN can identify and classify lung cancer types with greater accuracy in a shorter period. But as CNN is a DL model, it needs a massive amount of data, so if the dataset is insufcient, it will not give benchmark accuracy. We recommend that strategies based on diferent CNN architectures and CNN+ and other dimensional CNN must be inquired. (v) When patients are breathing, their lung shape changes, and it varies from patient to patient. Te patient's lung cancer cells appeared in large numbers, and there were more irregular shapes than in healthy lungs. Availability of all datasets is needed to measure all kinds of lungs. We recommend investigating all datasets and measuring diferent shapes of lungs. Te authors in [419,420] have already started working on this idea.

Conclusions
Lung cancer is the most widely recognized disease-related reason for death among people. Early detection of pulmonary nodules and lung cancer saves lives because it is known that the chances of surviving cancer are higher if it is found, diagnosed, and treated quickly. Several methods and systems have been proposed for analyzing pulmonary nodules in medical images. Additionally, the domain covers biological, engineering, computer science, and histological research. However, this article provides a comprehensive overview of the lung cancer detection interface. It is intended for novices interested in learning about the present state of lung cancer detection methods and technologies. Te essential concepts of lung cancer detection methods are fully explored. Te article focuses on many aspects of the research domain, including image preprocessing, feature extraction, segmentation, feature selection methodologies, performance measurements, and challenges and limitations along with the possible solutions. Te article endorses a summary of current methods to help new researchers quickly understand the research domain conceptions. Te study also looks into the various types of datasets available to lung cancer All datasets need to be available openly. Additionally, research should be conducted utilizing such imaging modalities using unpublished datasets. All datasets should be disclosed for future research works and implementations.
Accurate segmentation Segmentation models are not properly executed All segmentation models need to be implementing in various modalities which may uplift the lung nodule and cancer detection results Nodule size and types Small nodules are needed to be detected more efciently All kinds of nodules need to be investigated. Implementing feature extraction and selection can detect most of the nodules. Nodules can be identifed by feature and classifer selection.
Efcient CADe system Nodules and cancer detection need to be more accurate using all architectures Random forest, SVM, DBN with RM, and CNNs are mostly used for lung cancer diagnosis. ML and DL networks of other kinds should be analyzed in this feld.
Volumetric measurements All lung image shapes are not the same. So, all datasets need to be extracted.
When patients are breathing, their lung shape changes and it varies from patient to patient. We recommend investigating all datasets and measuring diferent shapes of lungs. detection systems. Te fundamental principles of lung cancer detection and nodule classifcation procedures are thoroughly explored using CT scan, MRI, or X-ray imaging. Furthermore, the article combines current cancer-detecting systems, describing a preliminary review based on previous works. Te article also describes the challenges and limitations that will help explore the inconvenience of lung cancer detection technologies. Te majority of lung cancer detection methods are now in the primary stages of development. Still, there are many things that could be changed to make the system work better. Te combined eforts of scientifc researchers and the tech sectors are required to commercialize this vast area for the beneft of ordinary people.

Data Availability
No data were used to support this study.

Conflicts of Interest
Te authors declare that they have no conficts of interest.