Classification of Brain Tumor Images Using CNN

A brain tumor is a serious malignant condition caused by unregulated as well as aberrant cell partitioning. Recent advances in deep learning have aided the healthcare business, particularly, diagnostic imaging for the diagnosis of numerous disorders. The most frequent and widely utilized machine learning model for image recognition is probably task CNN. Similarly, in our study, we categorize brain MRI scanning images using CNN and data augmentation and image processing techniques. We compared the performance of the scratch CNN model with that of pretrained VGG-16 models using transfer learning. Even though the investigation is carried out on a small dataset, the results indicate that our model's accuracy is quite successful and has extremely low complexity rates, achieving 100 percent accuracy compared to 96 percent accuracy for VGG-16. Compared to existing pretrained methods, our model uses much less processing resources and produces substantially greater accuracy.


Introduction
Te human body is composed of several types of cells.Each cell has a specifc role.To produce new cells, the body's cells develop and divide methodically.Tese new cells contribute to the health and function of the human body.When cells lose the capacity to regulate their own growth, they expand in an uncontrolled manner.A tumor is the mass of extracellular tissue that forms a tumor.Tumors might be benign or malignant.Malignant tumors cause cancer, whereas benign tumors are not carcinogenic.Medical image data acquired from diverse biomedical devices employing different imaging modalities are essential diagnostic factors.Magnetic resonance imaging (MRI) is a technology that detects magnetic fux vectors and radiofrequency pulses in the nuclei of hydrogen atoms in the water molecules of a patient's body.Diagnostically, the MRI scan is better than the CTscan since it does not employ radiation.Te MRI may be used by radiologists to assess the brain.Te MRI is capable of detecting the existence of brain tumors [1,2].In addition, operator engagement in the MRI may result in inaccurate categorization due to noise.Due to the vast amount of MRI data that must be analyzed, less costly automated approaches are necessary.A great degree of precision is essential when dealing with human life, which is why the automatic diagnosis of malignancies on MR images is crucial.Using supervised and unsupervised machine learning algorithm techniques, it is possible to classify MR pictures of the brain as normal or pathological.Using machine learning methods, this work provides an efective automated categorization approach for brain MRI data.Te supervised machine learning approach is used to classify MR images of the brain [3,4].

Literature Review
Avs ¸ar and Salcin [5] proposed an approach for identifying early-stage brain tumors.MRI images were analyzed to detect tumor-containing regions and classify these regions according to a variety of tumor types.For picture categorization, deep learning yields rather excellent results.As a consequence, the CNN technique was used and implemented utilizing the TensorFlow framework in this study.It has been proved that the faster CNN method can obtain a 91.66 percent accuracy rate, which is higher than earlier research.Sarkar et al. [6] proposed using MRI scans to identify the kind of brain tumor.Meningioma, glioma, and pituitary tumors were identifed with an accuracy rate of 91.3% using a 2D CNN for classifcation.Te study dataset included information on the three most prevalent forms of brain tumors.
Ranjbarzadeh et al. [7] suggested a fexible and efective approach for segmenting brain tumors.Tis technique decreases processing time while resolving overftting difculties in a cascading deep learning model.Using two independent routes, this CNN model captures both locally and globally relevant properties.In addition, the accuracy of tumor segmentation was greatly improved in comparison to current models.Our proposed method produces average WT, growing tumor, and total tumor core dice values of 0.9203, 0.9113, and 0.8726, respectively.Kokila et al. [8] created a model to detect brain tumors using MRI.It involves locating the tumor, establishing its grade and kind, and pinpointing its position.Instead of using a distinct model for each classifcation task, our technique organized brain MRI data for many classifcation tasks using a single model.CNN is capable of categorizing and detecting tumors; hence, the multitask classifcation relies on CNN's classifcation and detection abilities.A CNN-based algorithm may also be used to determine the location of brain tumors with an accuracy of 92 percent.
Te regularized extreme learning machine was utilized by Gumaei et al. [9] to create the brain tumor division (RELM).Photos were frst preprocessed so that the algorithm could comprehend them simply.For preprocessing, the system adopted the min-max approach.Tis min-max preprocessing technique was highly efective in boosting the brightness of input images.
Kaplan et al. [10] used both methods to classify and detect brain tumors.Te frst suggested method was the local binary pattern (LBP) centered on the neighborhood distance relation known as nLBP, as well as the second method known as LBP centered on the angles between neighbors.Tese two techniques were used to preprocess MRIs of the three most prevalent forms of brain tumors: glioma, meningioma, and pituitary tumor.For character development, preprocessed picture statistics were used.Tis revised model outperformed conventional feature extraction strategies.
Pallavi et al. [11] used an automated method of deep CNN modelling, which is made up of six learnable layers and helps in automating feature learning from MRI images of the brain.Tis method involves little preprocessing and does not employ handmade features, and the proposed method may be applied for various MRI classifcations.

Proposed Methodology
Troughout this work, 253 MRI images were processed using image processing algorithms [12].We trained neurons using a basic 8-layer CNN model and compared the results of our CNN model built from scratch with those of a VGG-16 machine utilizing transfer learning.Te collection contains 155 images of malignant tumors and 98 shots of benign, noncancerous tumors.Our data were categorized into three groups: learning, verifcation, and evaluation.Te machine learning technique may be used to train models, whereas the test dataset is often used to analyze models and alter parameters.Finally, the test results would be utilized to assess the models.Our proposed technique is broken into many steps.Figure 1 shows an overview of the proposed approach [12].

Image Processing.
We used an open-source software computer vision-(CV-) based Canny edge detection [13] technique to extract just the brain area from MRI data.Tis would be a multiphase approach for detecting the edges of objects in photographs.Figure 2 displays the True MRI brain boundaries as determined by the aforementioned method, with just the brain portion of the pictures cropped [13].
3.2.Data Enhancement.Data augmentation would be a method of intentionally boosting the volume and variety of known information [14].We understand that fne-tuning the parameters of a deep learning model requires a vast quantity of data.However, since our dataset remains tiny, we used the data preprocessing approach [15] upon our training sample, introducing small adjustments to our images including fips, rotations, and intensity.Tis would improve the volume of our dataset, and our model would treat every one of these little changes as a separate image, allowing the model to learn and function well.Figure 3 shows a collection of augmented images derived from a single image [15].

CNN Model.
In this work, we suggested a basic CNN model and used it to retrieve enhanced MRI data images with an RGB color channel and a batch size comprising 32.We started as a single 16-flter convolution layers with a flter size � 3333.Te purpose for using just 16 flters is to identify corners, edges, as well as lines.Next, we inserted a maxpooling level with a 2222 fltering to extract the maximum summation of that image; subsequently, we expanded the convolution layers and flters up to 32, 64, and subsequently 128, with almost the same size of flter, i.e., 3333.As even the quantity of flters rises, this merges these little patterns to create larger patterns such as circles, squares, and so on.To make best of the situation, we added max-pooling layers of convolution layer.Furthermore, we used a completely integrated dense layer of 256 neurons in conjunction with a softmax output level to compute the probability estimate for every class and classify the ultimate decision labeling as Yes/No based on whether the incoming MRI image includes cancer or just does not contain tumor.Te confguration of our suggested CNN architecture can be seen in Figure 4

Results and Discussion
We conducted experiments on the brain tumor MRI images database published by Chakrabarty [12].Te collection, which is open to the public, comprises of 253 genuine brain scans created by radiologists employing data from actual

Conclusion
A unique categorization strategy for brain tumors is being ofered in this work.First, we detected the ROI in MRI images using image edge detection and then cropped them [19].Following that, we used the data preparation procedure to expand the size of our datasets.Second, we propose a basic CNN network as an efective classifcation approach for brain tumors.Neural network training needs a signifcant amount of information for complex and accurate outputs; however, our experimental outcomes demonstrate that we can achieve 100% accuracy even with a very tiny dataset, as our accuracy rate is higher than that of VGG-16.Our suggested approach may aid in the prognostic relevance of tumor identifcation in individuals with brain malignancies [20].As we have used a large amount of information, it took more time to calculate the results in comparison to traditional brain tumor classifcation models which we will handle in future work.
[16].3.4.Transfer Learning.Rather than constructing a new CNN model, deep learning uses a pretrained CNN classifcation algorithm that has already been trained on a big dataset such as "ImageNet."Pan and Yang [16] presented a paradigm for gaining a better understanding of learning algorithms.Transfer learning takes advantage of past information rather than starting the learning program from new.Considering 2 Computational Intelligence and Neuroscience our dataset was so small, we utilized a pretrained VGG-16 CNN model that was perfectly alright by freezing parts of the layers to minimize the ftting problem.Karen Simonyan as well as Andrew Zisserman introduced the VGG-16 CNN architecture, comprising 16 convolutional layers, during 2014 [17].Tis same input geometry of the network image would be as input.All across the network, it has 16 convolution layers and a flter size of equivalent to 3333 and also having 5 pooling layers with a size equal to 2222.Two entirely connected layers with just a softmax output level sit on top.With almost 138 million variables, the VGG-16 model would be a massive network.It builds deep neural networks by layering several convolutional layers, which improves the capacity for learning hidden characteristics.

Figure 2 :
Figure 2: Detecting edges utilizing edge detection as well as cropping the brain section [13].