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Article

Analysis of Features of Alzheimer’s Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network

1
Department of Multimedia Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania
2
Department of Applied Informatics, Vytautas Magnus University, 44248 Kaunas, Lithuania
*
Author to whom correspondence should be addressed.
Academic Editor: Markos G. Tsipouras
Diagnostics 2021, 11(6), 1071; https://doi.org/10.3390/diagnostics11061071
Received: 18 May 2021 / Revised: 4 June 2021 / Accepted: 8 June 2021 / Published: 10 June 2021
(This article belongs to the Special Issue Machine Learning Approaches for Neurodegenerative Diseases Diagnosis)
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. View Full-Text
Keywords: Alzheimer disease; mild cognitive impairment; magnetic resonance imaging; deep learning; residual neural network Alzheimer disease; mild cognitive impairment; magnetic resonance imaging; deep learning; residual neural network
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MDPI and ACS Style

Odusami, M.; Maskeliūnas, R.; Damaševičius, R.; Krilavičius, T. 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 2021, 11, 1071. https://doi.org/10.3390/diagnostics11061071

AMA Style

Odusami M, Maskeliūnas R, Damaševičius R, Krilavičius T. 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. 2021; 11(6):1071. https://doi.org/10.3390/diagnostics11061071

Chicago/Turabian Style

Odusami, Modupe, Rytis Maskeliūnas, Robertas Damaševičius, and Tomas Krilavičius. 2021. "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 11, no. 6: 1071. https://doi.org/10.3390/diagnostics11061071

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