Analysis of Features of Alzheimer’s Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network
One of the first signs of Alzheimer’s disease (AD) is mild cognitive impairment (MCI), in which there are small variants of brain changes among the intermediate stages. Although there has been an increase in research into the diagnosis of AD in its early levels of developments lately, brain changes,...
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MDPI AG
2021-06-01
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Series: | Diagnostics |
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Online Access: | https://www.mdpi.com/2075-4418/11/6/1071 |
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author | Modupe Odusami Rytis Maskeliūnas Robertas Damaševičius Tomas Krilavičius |
author_facet | Modupe Odusami Rytis Maskeliūnas Robertas Damaševičius Tomas Krilavičius |
author_sort | Modupe Odusami |
collection | DOAJ |
description | One of the first signs of Alzheimer’s disease (AD) is mild cognitive impairment (MCI), in which there are small variants of brain changes among the intermediate stages. Although there has been an increase in research into the diagnosis of AD in its early levels of developments lately, brain changes, and their complexity for functional magnetic resonance imaging (fMRI), makes early detection of AD difficult. This paper proposes a deep learning-based method that can predict MCI, early MCI (EMCI), late MCI (LMCI), and AD. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) fMRI dataset consisting of 138 subjects was used for evaluation. The finetuned ResNet18 network achieved a classification accuracy of 99.99%, 99.95%, and 99.95% on EMCI vs. AD, LMCI vs. AD, and MCI vs. EMCI classification scenarios, respectively. The proposed model performed better than other known models in terms of accuracy, sensitivity, and specificity. |
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format | Article |
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issn | 2075-4418 |
language | English |
last_indexed | 2024-03-10T10:31:45Z |
publishDate | 2021-06-01 |
publisher | MDPI AG |
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series | Diagnostics |
spelling | doaj.art-e888a879620f40d48d22742d40c01eba2023-11-21T23:36:41ZengMDPI AGDiagnostics2075-44182021-06-01116107110.3390/diagnostics11061071Analysis of Features of Alzheimer’s Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 NetworkModupe Odusami0Rytis Maskeliūnas1Robertas Damaševičius2Tomas Krilavičius3Department of Multimedia Engineering, Kaunas University of Technology, 44249 Kaunas, LithuaniaDepartment of Multimedia Engineering, Kaunas University of Technology, 44249 Kaunas, LithuaniaDepartment of Applied Informatics, Vytautas Magnus University, 44248 Kaunas, LithuaniaDepartment of Applied Informatics, Vytautas Magnus University, 44248 Kaunas, LithuaniaOne of the first signs of Alzheimer’s disease (AD) is mild cognitive impairment (MCI), in which there are small variants of brain changes among the intermediate stages. Although there has been an increase in research into the diagnosis of AD in its early levels of developments lately, brain changes, and their complexity for functional magnetic resonance imaging (fMRI), makes early detection of AD difficult. This paper proposes a deep learning-based method that can predict MCI, early MCI (EMCI), late MCI (LMCI), and AD. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) fMRI dataset consisting of 138 subjects was used for evaluation. The finetuned ResNet18 network achieved a classification accuracy of 99.99%, 99.95%, and 99.95% on EMCI vs. AD, LMCI vs. AD, and MCI vs. EMCI classification scenarios, respectively. The proposed model performed better than other known models in terms of accuracy, sensitivity, and specificity.https://www.mdpi.com/2075-4418/11/6/1071Alzheimer diseasemild cognitive impairmentmagnetic resonance imagingdeep learningresidual neural network |
spellingShingle | Modupe Odusami Rytis Maskeliūnas Robertas Damaševičius Tomas Krilavičius Analysis of Features of Alzheimer’s Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network Diagnostics Alzheimer disease mild cognitive impairment magnetic resonance imaging deep learning residual neural network |
title | Analysis of Features of Alzheimer’s Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network |
title_full | Analysis of Features of Alzheimer’s Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network |
title_fullStr | Analysis of Features of Alzheimer’s Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network |
title_full_unstemmed | Analysis of Features of Alzheimer’s Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network |
title_short | Analysis of Features of Alzheimer’s Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network |
title_sort | analysis of features of alzheimer s disease detection of early stage from functional brain changes in magnetic resonance images using a finetuned resnet18 network |
topic | Alzheimer disease mild cognitive impairment magnetic resonance imaging deep learning residual neural network |
url | https://www.mdpi.com/2075-4418/11/6/1071 |
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