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.2021 Apr 27:2021:5514839.
doi: 10.1155/2021/5514839. eCollection 2021.

Diagnosis of Alzheimer's Disease Severity with fMRI Images Using Robust Multitask Feature Extraction Method and Convolutional Neural Network (CNN)

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Diagnosis of Alzheimer's Disease Severity with fMRI Images Using Robust Multitask Feature Extraction Method and Convolutional Neural Network (CNN)

Morteza Amini et al. Comput Math Methods Med..

Abstract

The automatic diagnosis of Alzheimer's disease plays an important role in human health, especially in its early stage. Because it is a neurodegenerative condition, Alzheimer's disease seems to have a long incubation period. Therefore, it is essential to analyze Alzheimer's symptoms at different stages. In this paper, the classification is done with several methods of machine learning consisting ofK-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), linear discrimination analysis (LDA), and random forest (RF). Moreover, novel convolutional neural network (CNN) architecture is presented to diagnose Alzheimer's severity. The relationship between Alzheimer's patients' functional magnetic resonance imaging (fMRI) images and their scores on the MMSE is investigated to achieve the aim. The feature extraction is performed based on the robust multitask feature learning algorithm. The severity is also calculated based on the Mini-Mental State Examination score, including low, mild, moderate, and severe categories. Results show that the accuracy of the KNN, SVM, DT, LDA, RF, and presented CNN method is 77.5%, 85.8%, 91.7%, 79.5%, 85.1%, and 96.7%, respectively. Moreover, for the presented CNN architecture, the sensitivity of low, mild, moderate, and severe status of Alzheimer patients is 98.1%, 95.2%,89.0%, and 87.5%, respectively. Based on the findings, the presented CNN architecture classifier outperforms other methods and can diagnose the severity and stages of Alzheimer's disease with maximum accuracy.

Copyright © 2021 Morteza Amini et al.

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Conflict of interest statement

The authors declare that there are no financial or other conflicts of interest in this research and its publication.

Figures

Figure 1
Figure 1
The conceptual flowchart of the presented process.
Figure 2
Figure 2
Results of noise reduction using QMFT: (a) input image; (b) input image contour form; (c) noise-reduced image; (d) contour form of noise-reduced image.
Figure 3
Figure 3
The PSNR value of noise reduction from fMRI images.
Figure 4
Figure 4
The cumulative summation of sorted eigenvalues.
Figure 5
Figure 5
Confusion matrix of machine learning methods.
Figure 6
Figure 6
ROC curves of machine learning methods.
Figure 7
Figure 7
The accuracy and the loss value for the presented CNN architecture.
Figure 8
Figure 8
The confusion matrix of the presented CNN method.
See this image and copyright information in PMC

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