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An MRI Scans-Based Alzheimer’s Disease Detection via Convolutional Neural Network and Transfer Learning
Alzheimer’s disease (AD) is the most common type (>60%) of dementia and can wreak havoc on the psychological and physiological development of sufferers and their carers, as well as the economic and social development. Attributed to the shortage of medical staff, automatic diagnosis of AD has beco...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9318866/ https://www.ncbi.nlm.nih.gov/pubmed/35885437 http://dx.doi.org/10.3390/diagnostics12071531 |
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author | Chui, Kwok Tai Gupta, Brij B. Alhalabi, Wadee Alzahrani, Fatma Salih |
author_facet | Chui, Kwok Tai Gupta, Brij B. Alhalabi, Wadee Alzahrani, Fatma Salih |
author_sort | Chui, Kwok Tai |
collection | PubMed |
description | Alzheimer’s disease (AD) is the most common type (>60%) of dementia and can wreak havoc on the psychological and physiological development of sufferers and their carers, as well as the economic and social development. Attributed to the shortage of medical staff, automatic diagnosis of AD has become more important to relieve the workload of medical staff and increase the accuracy of medical diagnoses. Using the common MRI scans as inputs, an AD detection model has been designed using convolutional neural network (CNN). To enhance the fine-tuning of hyperparameters and, thus, the detection accuracy, transfer learning (TL) is introduced, which brings the domain knowledge from heterogeneous datasets. Generative adversarial network (GAN) is applied to generate additional training data in the minority classes of the benchmark datasets. Performance evaluation and analysis using three benchmark (OASIS-series) datasets revealed the effectiveness of the proposed method, which increases the accuracy of the detection model by 2.85–3.88%, 2.43–2.66%, and 1.8–40.1% in the ablation study of GAN and TL, as well as the comparison with existing works, respectively. |
format | Online Article Text |
id | pubmed-9318866 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93188662022-07-27 An MRI Scans-Based Alzheimer’s Disease Detection via Convolutional Neural Network and Transfer Learning Chui, Kwok Tai Gupta, Brij B. Alhalabi, Wadee Alzahrani, Fatma Salih Diagnostics (Basel) Article Alzheimer’s disease (AD) is the most common type (>60%) of dementia and can wreak havoc on the psychological and physiological development of sufferers and their carers, as well as the economic and social development. Attributed to the shortage of medical staff, automatic diagnosis of AD has become more important to relieve the workload of medical staff and increase the accuracy of medical diagnoses. Using the common MRI scans as inputs, an AD detection model has been designed using convolutional neural network (CNN). To enhance the fine-tuning of hyperparameters and, thus, the detection accuracy, transfer learning (TL) is introduced, which brings the domain knowledge from heterogeneous datasets. Generative adversarial network (GAN) is applied to generate additional training data in the minority classes of the benchmark datasets. Performance evaluation and analysis using three benchmark (OASIS-series) datasets revealed the effectiveness of the proposed method, which increases the accuracy of the detection model by 2.85–3.88%, 2.43–2.66%, and 1.8–40.1% in the ablation study of GAN and TL, as well as the comparison with existing works, respectively. MDPI 2022-06-23 /pmc/articles/PMC9318866/ /pubmed/35885437 http://dx.doi.org/10.3390/diagnostics12071531 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chui, Kwok Tai Gupta, Brij B. Alhalabi, Wadee Alzahrani, Fatma Salih An MRI Scans-Based Alzheimer’s Disease Detection via Convolutional Neural Network and Transfer Learning |
title | An MRI Scans-Based Alzheimer’s Disease Detection via Convolutional Neural Network and Transfer Learning |
title_full | An MRI Scans-Based Alzheimer’s Disease Detection via Convolutional Neural Network and Transfer Learning |
title_fullStr | An MRI Scans-Based Alzheimer’s Disease Detection via Convolutional Neural Network and Transfer Learning |
title_full_unstemmed | An MRI Scans-Based Alzheimer’s Disease Detection via Convolutional Neural Network and Transfer Learning |
title_short | An MRI Scans-Based Alzheimer’s Disease Detection via Convolutional Neural Network and Transfer Learning |
title_sort | mri scans-based alzheimer’s disease detection via convolutional neural network and transfer learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9318866/ https://www.ncbi.nlm.nih.gov/pubmed/35885437 http://dx.doi.org/10.3390/diagnostics12071531 |
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