Diagnosis of Alzheimer's disease severity with fMRI images using robust multitask feature extraction method and convolutional neural network (CNN)
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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 of K -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.
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DNN classifiers have shown superior performance compared to traditional machine learning methods such as support vector machines and random forest for accurate classification of disease groups using neuroimaging data.
“DNN classifiers have shown superior performance compared to traditional machine learning methods, such as support vector machines (SVM) and random forest for accurate classification of disease groups using neuroimaging data (Schmidhuber, 2015; Eslami and Saeed, 2019; Amini et al., 2021).”
From Differential diagnosis of frontotemporal dementia subtypes with explainable deep learning on structural MRI (2024) · cites this paper as Amini et al., 2021
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- Authors
- Amini, M., Pedram, M. M., Moradi, A., & Ouchani, M.
- Affiliations
- Institute for Cognitive Science Studies (IR)
- Kharazmi University (IR)
- Shahid Beheshti University (IR)
- Corresponding author
- Pedram, Mir Mohsen
- Venue
- Computational and Mathematical Methods in Medicine
- Publisher
- Wiley
- Published
- 2021-04-28
- Volume
- 2021
- Pages
- 1-15
- ISSN
- 1748-670X (ISSN-L)
- 1748-6718
- PMC
- PMC8100410
- OpenAlex
- W3157749950
- Cited by
- 84
- References
- 64
- Citations by year
- 2026: 11 · 2025: 13 · 2024: 19 · 2023: 18 · 2022: 16 · 2021: 7
- FWCI
- 10.76
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Computational and Mathematical Methods in Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- Alzheimer's disease, disease severity classification, functional MRI, convolutional neural networks, feature extraction, Mini-Mental State Examination, early diagnosis, neurodegenerative diseases
- MeSH terms
- Mental Status and Dementia Tests, Deep Learning, Algorithms, Alzheimer Disease, Brain, Decision Trees, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Severity of Illness Index, Discriminant Analysis, Databases, Factual, Neural Networks, Computer, Computational Biology, Early Diagnosis, Functional Neuroimaging, Support Vector Machine
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