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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Main Authors: Modupe Odusami, Rytis Maskeliūnas, Robertas Damaševičius, Tomas Krilavičius
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:Diagnostics
Subjects:
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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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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