Smart healthcare has undergone new opportunities and challenges with the arrival of the Industry 4.0 era. The intelligent imaging diagnosis system is a staple part of smart healthcare, helping doctors make clinical decisions. Nevertheless, intelligent diagnosis analysis is still confronted with the issue that it is challenging to extract effective features from the limited and high-dimensional data, particularly in resting-state data of amnesic mild cognitive impairment (aMCI). Furthermore, the intelligent imaging diagnosis system for aMCI is conductive to make timely predicting groups that may convert to Alzheimer’s disease (AD). To improve the system’s detection performance and reduce its data redundancy, we first develop an adaptive structure feature generation strategy (ASFGS) based on the Laplacian matrix and sparse autoencoder to obtain the structural features of brain functional network (BFN). Concurrently, we present a multiscale local feature detection strategy (MLFDS) to overcome the low utilization of local features of BFN. And finally, multiscale features, including structural features and multiscale local features, are fused by concatenation method to further improve the detection performance of aMCI system. Support vector machine based on radial basis function (RBF-SVM) for small data learning is adopted to evaluate the effectiveness of the proposed features. Besides, we employ leave-one-out cross-validation strategy to avoid the overfitting problem of classifier training process. The experiment results elucidate that the accuracy (ACC) and the area under the curve (AUC) in this work provide 86.57% and 86.36%, respectively, which outperforms the traditional methods and offers new insights for accuracy requirements of the aMCI system.
Industry 4.0, represented by improvement of the intelligent level of the manufacturing industry, is profoundly converting all walks of life. Smart healthcare that adopts various Industry 4.0 concepts is an era full of opportunities and challenges [
Nowadays, brain-related diseases are considered as one of the most severe problems in the healthcare system. Alzheimer’s disease (AD), which frequently occurs in the elderly population, is a disease accompanied by cognitive decline and noncognitive mental symptoms [
In this work, the correlation value between the time series of the standard brain regions is calculated using the Pearson correlation coefficient, thus constructing the brain functional network (BFN) that reflects the interaction between the nodes. Significantly, the existing methods only use the local features of the BFN as the input of classifier while ignoring its structural features. To address this issue, we develop an adaptive structure feature generation strategy (ASFGS) based on the Laplacian matrix and sparse autoencoder to improve the classification performance and reduce data redundancy of the system. Concurrently, we present a multiscale local feature detection strategy (MLFDS) to overcome the low utilization of local features of BFN. Afterwards, multiscale features, including structural features and multiscale local features, are fused to further improve classification accuracy of aMCI. It is worth mentioning that support vector machine based on radial basis function (RBF-SVM) for small data learning is utilized to evaluate the performance of the proposed algorithm. In the following, we employ the leave-one-out cross-validation strategy to avoid the overfitting problem of classifier.
Accordingly, the innovativeness of our work is that we first present an ASFGS algorithm to obtain the structural features of BFN, improve the detection accuracy, and reduce data redundancy of the system. Then, we develop an MLFDS algorithm to excavate the local features of BFN at multiple scales. Finally, multiscale features of BFN obtained from the ASFGS algorithm and MLFDS algorithm are concatenated to further improve classification accuracy of aMCI. The results elucidate that the accuracy (ACC) and the area under the curve (AUC) in this work provide about 86.57% and 86.36%, respectively, which outperforms the state-of-the-art methods. It can be inferred that our work dramatically improves the detection performance of aMCI system, providing a new perspective for the construction of intelligent imaging diagnosis system in smart healthcare.
The rest of the work is structured as follows: In Section
The recent development and combination of machine learning, statistical algorithm, and neuroimaging technology offer a new perspective for designing an intelligent imaging diagnosis system, which is a crucial procedure toward smart health. The design of an intelligent imaging diagnosis system mainly includes several parts, including the data generation module, data preprocessing module, feature learning module, classifier training module, and feedback module [
Numerous works about BFN research have focused on using rs-fMRI to excavate effective features of aMCI. For example, the altered patterns of rich club generated from the BFN have been reported in [
With the rapid development of machine learning technology, feature extraction and classification algorithms related to disease have become a hot spot. However, due to the limited number of aMCI data, feature selection is first conducted to reduce redundant information and then use them as the input to classifier to improve classification performance. It provides about the ACC of 69.00% when the significant regional signals resulting from brain pathway activities are employed as the input of support vector machine (SVM) classifier, providing new opportunities for comprehending the disrupted patterns caused by disease [
In present work, we present an ASFGS algorithm using the Laplacian matrix and sparse autoencoder to obtain the structural features of BFN. Concurrently, we develop an MLFDS algorithm to overcome the low utilization of local features of BFN. In the end, all the features generated above are concatenated to improve the classification performance of aMCI system.
The critical point of our work is to design the reliable detection system of aMCI from commonly redundant information of rs-fMRI data, as is shown in Figure
Overall framework of aMCI detection system.
We utilize the public dataset downloaded from the second phase of Alzheimer’s Disease Neuroimaging Initiative (ADNI-2) to validate effectiveness of the proposed aMCI detection system (downloaded from
To better extract the time signals of the corresponding brain regions, anatomical automatic labeling (AAL) template is employed to segment the whole brain into 90 regions of interest (ROI) [
We develop an ASFGS algorithm for extracting the structural features of BFN, which is aimed at improving the detection performance of aMCI system. ASFGS algorithm is mainly composed of two components, including the rough feature extraction module and accurate feature extraction module, as shown in Figure
ASFGS algorithm for extracting structural features of BFN.
Considering the substantial contribution of Laplacian Eigenmaps (LE) to maintain and reflect the local relationship between data to some extent, we present the rough feature extraction module based on its conception to extract the structural features of BFN. A brief description of the LE algorithm is as follows [
Given a set of data
Notably, the degree matrix represents the sum of each column or row in
Since the Laplace matrix is a positive semidefinite matrix, it can be further expressed as:
To maintain the adjacency relation between two data, it can be converted to the minimization issue. That is, if
Ultimately, by employing the Lagrange multiplier method, it can be approximately converted into
Nevertheless, the objective of the LE algorithm is to reduce the dimension of data features, while the retained dimension is determined by the number of minimum nonzero eigenvalues of the matrix, which does not conform to the requirements of the structural feature extraction of BFN in this paper. Fortunately, LE algorithm plays a significant role in maintaining the relation between sample points after dimensionality reduction. Therefore, we present a rough feature extraction module that modifies the LE algorithm to put its proper focus on structural feature extraction of BFN.
First of all, the minimization problem is constructed using the Laplacian matrix [
Considering the limitation of the small data set, we reduce the dimension of BFN from
The accurate feature extraction module consists of a hidden layer, and the transfer functions of the encoder and decoder are nonlinear. First, the cost function of the sparse autoencoder using sparse constraint in the hidden layer is given as follows [
Here, let
In order to optimize the error between the output and input of the sparse autoencoder, the back propagation algorithm is employed to update the model parameters. Owing to the limited number of data, we further employ the two-sample
We develop an MLFDS algorithm to overcome the low utilization of local features of BFN. Our algorithm is proposed based on maximizing the mean difference between classes and minimizing the intraclass variance. The framework of the MLFDS algorithm shown in Figure
The MLFDS algorithm to extract the multiscale local features.
At present, the two-sample
We first calculate the variable coefficient of BFN in two groups to generate the mask, respectively. The value of corresponding position is 0 if variable coefficient in the mask is greater than the mean; otherwise, it is 1, where 1 means that variation coefficient is lower than the average value of variation coefficient. It is remarkable that we employ the median of variation coefficient as the average value to avoid the influence of extreme values. Then, we intersect the generation masks of the two kinds of data generated by the above operation, and the obtained mask is denoted as
We calculate the average value of
Perform Step 1 and Step 2
The principle of MLFDS algorithm is to select the position with lower variance under the premise of the obvious difference in mean value between groups. Therefore, the
In order to improve the detection performance of aMCI system, we concatenate multiscale features resulted from the ASFGS algorithm and MLFDS algorithm in this work. That is, supposing the dimensions of two groups of features are
In view of the finite data, the adoption of suitable classifier is essential to estimate the validation of features obtained from the proposed algorithms. Fortunately, numerous works on mild cognitive impairment (MCI) classification have shown that RBF-SVM classifier has superior detection performance [
The essence of SVM algorithm is to work around the optimization problem of the objective function [
The objective of Gaussian radial basis function is to obtain the new space, which is more favourable to classification [
To prevent the overfitting issue of classifier training process, we utilize the leave-one-out cross-validation strategy in this work.
To measure the performance of the classification model, the frequently used metrics for binary classification are ACC, F1-score, AUC, etc. Significantly, false positive (FP), false negative (FN), true negative (TN), and true positive (TP) are defined using the confusion matrix, as shown in Figure
The confusion matrix which is utilized to evaluate the binary classification.
This work explores the detection power of aMCI system using multiscale features of BFN, which are derived from rs-fMRI data, for the automatic identification and classification of aMCI subjects from HCs. In the proposed detection system, we employ structural features using ASFGS algorithm and multiscale local features using MLFDS algorithm to train an RBF-SVM classifier for accurate discrimination of aMCI individuals.
The rough feature extraction module is first presented to extract the structural features of BFN, which maintains the correlation between the brain nodes after dimensionality reduction from
The structural feature analysis using ASFGS algorithm.
Algorithm | ACC | F1-score | AUC |
---|---|---|---|
ASFGS | 61.20% | 60.61% | 62.12% |
Our goal is to simulate the information processing pattern of human brain to extract structural information of BFN, which further improves the detection performance of the aMCI system. Consequently, mathematical modelling about BFN is implemented, which contains information about the interactions between brain regions [
Reconstruction error of sparse autoencoder at each threshold.
We develop an MLFDS algorithm to excavate the multiscale local features of the BFN. Specifically, we first present the VC-MG strategy to generate the mask, and then, we present the MMDGS strategy based on the mask to extract the multiscale local features.
We extract the local fusion features of BFN using MLFDS2 algorithm to improve the detection performance of aMCI system. The results elucidate that five pairs of connected brain nodes with obvious alteration are found using MLFDS1 algorithm, including (21, 72), (45, 46), (11, 61), (73, 76), and (74, 76), as shown in Figure
The brain nodes involved in multiscale local feature selection: (a) MLFDS1 algorithm; (b) SLF algorithm.
As shown in Table
The multiscale local feature analysis using MLFDS algorithm.
Algorithm | ACC | F1-score | AUC |
---|---|---|---|
SLF | 76.12% | 75.76% | 76.47% |
MLFDS1 | 79.10% | 77.42% | 79.14% |
MLFDS2 | 80.60% | 80.00% | 84.22% |
In order to further improve the detection of aMCI system, we mainly concatenate the multiscale features of BFN, referred to as ASFGS-MLFDS, including structural features and multiscale local features.
As is shown in Table
The fusion analysis of structural features and multiscale local features.
Algorithm | ACC | F1-score | AUC |
---|---|---|---|
SLF | 76.12% | 75.76% | 76.47% |
ASFGS-MLFDS | 86.57% | 85.71% | 86.36% |
In this paper, we develop an aMCI detection system. Firstly, we present the ASFGS algorithm to extract structural features of BFN. Then, we present the MLFDS algorithm that excavates the multiscale local features of BFN, thus overcoming the low utilization of local features. In the end, multiscale features of BFN, including structural features and multiscale local features, are fused to further improve the detection performance of aMCI system. Our work outperforms the state-of-the-art methods and offers new insights for the accuracy requirement of aMCI system. Accordingly, the ASFGS algorithm and MLFDS algorithm we present can be employed to detect brain diseases, providing new insights for the intelligent construction of the imaging diagnosis system. The future work contains introducing multimodality data to improve the detection performance of aMCI system.
The data used to support the findings of this study are available from the corresponding author upon request.
The authors declare that they have no conflicts of interest.
This work has been supported by the Natural Science Foundation of Fujian Province of China (No. 2017J01372), the Foundation of Fujian Educational Committee (No. JK2015019), the National 135 Key R $\&$ D Program Projects (Grant No. 2018YFB1600600), the Tsinghua Overseas Research Cooperation Project (Grant No. HW2020005), and the Science and Technology Innovation Committee of Shenzhen Project (Grant No. JCYJ20190813173401651).