Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning

Abstract

Purpose: Early diagnosis or detection of Alzheimer's disease (AD) from the normal elder control (NC) is very important. However, the computer-aided diagnosis (CAD) was not widely used, and the classification performance did not reach the standard of practical use. We proposed a novel CAD system for MR brain images based on eigenbrains and machine learning with two goals: accurate detection of both AD subjects and AD-related brain regions.Method: First, we used maximum inter-class variance (ICV) to select key slices from 3D volumetric data. Second, we generated an eigenbrain set for each subject. Third, the most important eigenbrain (MIE) was obtained by Welch's t-test (WTT). Finally, kernel support-vector-machines with different kernels that were trained by particle swarm optimization, were used to make an accurate prediction of AD subjects. Coefficients of MIE with values higher than 0.98 quantile were highlighted to obtain the discriminant regions that distinguish AD from NC.Results: The experiments showed that the proposed method can predict AD subjects with a competitive performance with existing methods, especially the accuracy of the polynomial kernel (92.36 ± 0.94) was better than the linear kernel of 91.47 ± 1.02 and the radial basis function (RBF) kernel of 86.71 ± 1.93. The proposed eigenbrain-based CAD system detected 30 AD-related brain regions (Anterior Cingulate, Caudate Nucleus, Cerebellum, Cingulate Gyrus, Claustrum, Inferior Frontal Gyrus, Inferior Parietal Lobule, Insula, Lateral Ventricle, Lentiform Nucleus, Lingual Gyrus, Medial Frontal Gyrus, Middle Frontal Gyrus, Middle Occipital Gyrus, Middle Temporal Gyrus, Paracentral Lobule, Parahippocampal Gyrus, Postcentral Gyrus, Posterial Cingulate, Precentral Gyrus, Precuneus, Subcallosal Gyrus, Sub-Gyral, Superior Frontal Gyrus, Superior Parietal Lobule, Superior Temporal Gyrus, Supramarginal Gyrus, Thalamus, Transverse Temporal Gyrus, and Uncus). The results were coherent with existing literatures.Conclusion: The eigenbrain method was effective in AD subject prediction and discriminant brain-region detection in MRI scanning.

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

“…Fourteen different algorithms were employed in [115][116][117][118][119][120][121][122][123][124][125][126]. The datasets of Alzheimer's disease and other forms of dementia have relatively small sample size.…”

“…Table 4 shows some studies in Alzheimer's disease and other forms of dementia via machine learning algorithms. The applications include diagnosis of Alzheimer's disease [115,116], diagnosis of dementias [117], and detection of Alzheimer's disease related regions [118], prediction of mild cognitive impairment patients for conversion to Alzheimer's disease [119,120], detection of dissociable multivariate morphological patterns [121], diagnosis of both Alzheimer's disease and mild cognitive impairment [122] and identification of genes related to Alzheimer's disease [125,126]. Alzheimer's disease: sensitivity = 85%, specificity = 82%, accuracy = 85%; Mild cognitive impairment: sensitivity = 84%, specificity = 81%, accuracy = 85% [125] Identification of genes related to Alzheimer's disease DT; QAR 33 90 genes are related to Alzheimer's disease [126] Identification of genes related to Alzheimer's disease ELM; RF; SVM 31 Sensitivity= 78.77%; Specificity= 83.1%; Accuracy = 74.67% DCNN = deep convolutional neural network; DT = decision tree; ELM = extreme learning machine; EM = expectation maximization; GA = genetic algorithm; LC = lasso classification; LDS = low density separation; LR = logistic regression; NBC = Naive Bayes classifier; QAR = quantitative association rules; RF = random forest; RLO = random linear oracle; RS = random subspace; SVM = support vector machine.…”

“…Due to the multitude of smart healthcare applications, only four applications in the field of diseases diagnosis, cardiovascular diseases [82][83][84][85][86][87][88][89][90][91][92][93][94][95][96][97][98][99], diabetes mellitus [100][101][102][103][104][105][106][107][108][109][110][111][112], Alzheimer's disease and other forms of dementias [113][114][115][116][117][118][119][120][121][122][123][124][125][126], and tuberculosis [127][128][129][130][13...…”

Deep Neural Network Classification method to Alzheimer?s Disease Detection

“…Zhang et al [6] proposed Alzeimer"s disease detection using 3D MRI based on eigenbrain and machine learning method. The maximum inter-class variance (ICV) was used to select key slices from 3D volumetric data and an eigenbrain set for each subject was generated in their study.…”

Pathological Brain Detection in Magnetic Resonance Imaging Scanning by Wavelet Entropy and Hybridization of Biogeography-Based Optimization and Particle Swarm Optimization

“…Finally, they achieved an overall accuracy of 91.8%. Zhang et al [24] suggested to use a 3D eigenbrain method to detect subjects and brain regions related to AD. The accuracy achieved 92.36 ± 0.94.…”