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Volume 10
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Article

Multi-Method Analysis of Medical Records and MRI Images for Early Diagnosis of Dementia and Alzheimer’s Disease Based on Deep Learning and Hybrid Methods

by
Badiea Abdulkarem Mohammed
1,2,*,
Ebrahim Mohammed Senan
3,
Taha H. Rassem
4,
Nasrin M. Makbol
2,
Adwan Alownie Alanazi
5,
Zeyad Ghaleb Al-Mekhlafi
5,
Tariq S. Almurayziq
5 and
Fuad A. Ghaleb
6
1
Department of Computer Engineering, College of Computer Science and Engineering, University of Ha’il, Ha’il 81481, Saudi Arabia
2
College of Computer Science and Engineering, Hodeidah University, Hodiedah 967, Yemen
3
Department of Computer Science, Hajjah University, Hajjah 967, Yemen
4
Faculty of Science and Technology, Bournemouth University, Poole BH12 5BB, UK
5
Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha’il, Ha’il 81481, Saudi Arabia
6
School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
*
Author to whom correspondence should be addressed.
Electronics 2021, 10(22), 2860; https://doi.org/10.3390/electronics10222860
Submission received: 11 October 2021 / Revised: 13 November 2021 / Accepted: 18 November 2021 / Published: 20 November 2021
(This article belongs to the Topic Machine and Deep Learning)
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Abstract

Dementia and Alzheimer’s disease are caused by neurodegeneration and poor communication between neurons in the brain. So far, no effective medications have been discovered for dementia and Alzheimer’s disease. Thus, early diagnosis is necessary to avoid the development of these diseases. In this study, efficient machine learning algorithms were assessed to evaluate the Open Access Series of Imaging Studies (OASIS) dataset for dementia diagnosis. Two CNN models (AlexNet and ResNet-50) and hybrid techniques between deep learning and machine learning (AlexNet+SVM and ResNet-50+SVM) were also evaluated for the diagnosis of Alzheimer’s disease. For the OASIS dataset, we balanced the dataset, replaced the missing values, and applied the t-Distributed Stochastic Neighbour Embedding algorithm (t-SNE) to represent the high-dimensional data in the low-dimensional space. All of the machine learning algorithms, namely, Support Vector Machine (SVM), Decision Tree, Random Forest and K Nearest Neighbours (KNN), achieved high performance for diagnosing dementia. The random forest algorithm achieved an overall accuracy of 94% and precision, recall and F1 scores of 93%, 98% and 96%, respectively. The second dataset, the MRI image dataset, was evaluated by AlexNet and ResNet-50 models and AlexNet+SVM and ResNet-50+SVM hybrid techniques. All models achieved high performance, but the performance of the hybrid methods between deep learning and machine learning was better than that of the deep learning models. The AlexNet+SVM hybrid model achieved accuracy, sensitivity, specificity and AUC scores of 94.8%, 93%, 97.75% and 99.70%, respectively.
Keywords: Alzheimer; dementia; t-SNE algorithm; machine learning; deep learning; hybrid techniques Alzheimer; dementia; t-SNE algorithm; machine learning; deep learning; hybrid techniques

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MDPI and ACS Style

Mohammed, B.A.; Senan, E.M.; Rassem, T.H.; Makbol, N.M.; Alanazi, A.A.; Al-Mekhlafi, Z.G.; Almurayziq, T.S.; Ghaleb, F.A. Multi-Method Analysis of Medical Records and MRI Images for Early Diagnosis of Dementia and Alzheimer’s Disease Based on Deep Learning and Hybrid Methods. Electronics 2021, 10, 2860. https://doi.org/10.3390/electronics10222860

AMA Style

Mohammed BA, Senan EM, Rassem TH, Makbol NM, Alanazi AA, Al-Mekhlafi ZG, Almurayziq TS, Ghaleb FA. Multi-Method Analysis of Medical Records and MRI Images for Early Diagnosis of Dementia and Alzheimer’s Disease Based on Deep Learning and Hybrid Methods. Electronics. 2021; 10(22):2860. https://doi.org/10.3390/electronics10222860

Chicago/Turabian Style

Mohammed, Badiea Abdulkarem, Ebrahim Mohammed Senan, Taha H. Rassem, Nasrin M. Makbol, Adwan Alownie Alanazi, Zeyad Ghaleb Al-Mekhlafi, Tariq S. Almurayziq, and Fuad A. Ghaleb. 2021. "Multi-Method Analysis of Medical Records and MRI Images for Early Diagnosis of Dementia and Alzheimer’s Disease Based on Deep Learning and Hybrid Methods" Electronics 10, no. 22: 2860. https://doi.org/10.3390/electronics10222860

APA Style

Mohammed, B. A., Senan, E. M., Rassem, T. H., Makbol, N. M., Alanazi, A. A., Al-Mekhlafi, Z. G., Almurayziq, T. S., & Ghaleb, F. A. (2021). Multi-Method Analysis of Medical Records and MRI Images for Early Diagnosis of Dementia and Alzheimer’s Disease Based on Deep Learning and Hybrid Methods. Electronics, 10(22), 2860. https://doi.org/10.3390/electronics10222860

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