Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review
Alzheimer's disease (AD) is a neurodegenerative disorder that causes memory degradation and cognitive function impairment in elderly people. The irreversible and devastating cognitive decline brings large burdens on patients and society. So far, there is no effective treatment that can cure AD,...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9939698/ https://www.ncbi.nlm.nih.gov/pubmed/36814932 http://dx.doi.org/10.3389/fncom.2023.1038636 |
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author | Zhao, Zhen Chuah, Joon Huang Lai, Khin Wee Chow, Chee-Onn Gochoo, Munkhjargal Dhanalakshmi, Samiappan Wang, Na Bao, Wei Wu, Xiang |
author_facet | Zhao, Zhen Chuah, Joon Huang Lai, Khin Wee Chow, Chee-Onn Gochoo, Munkhjargal Dhanalakshmi, Samiappan Wang, Na Bao, Wei Wu, Xiang |
author_sort | Zhao, Zhen |
collection | PubMed |
description | Alzheimer's disease (AD) is a neurodegenerative disorder that causes memory degradation and cognitive function impairment in elderly people. The irreversible and devastating cognitive decline brings large burdens on patients and society. So far, there is no effective treatment that can cure AD, but the process of early-stage AD can slow down. Early and accurate detection is critical for treatment. In recent years, deep-learning-based approaches have achieved great success in Alzheimer's disease diagnosis. The main objective of this paper is to review some popular conventional machine learning methods used for the classification and prediction of AD using Magnetic Resonance Imaging (MRI). The methods reviewed in this paper include support vector machine (SVM), random forest (RF), convolutional neural network (CNN), autoencoder, deep learning, and transformer. This paper also reviews pervasively used feature extractors and different types of input forms of convolutional neural network. At last, this review discusses challenges such as class imbalance and data leakage. It also discusses the trade-offs and suggestions about pre-processing techniques, deep learning, conventional machine learning methods, new techniques, and input type selection. |
format | Online Article Text |
id | pubmed-9939698 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-99396982023-02-21 Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review Zhao, Zhen Chuah, Joon Huang Lai, Khin Wee Chow, Chee-Onn Gochoo, Munkhjargal Dhanalakshmi, Samiappan Wang, Na Bao, Wei Wu, Xiang Front Comput Neurosci Neuroscience Alzheimer's disease (AD) is a neurodegenerative disorder that causes memory degradation and cognitive function impairment in elderly people. The irreversible and devastating cognitive decline brings large burdens on patients and society. So far, there is no effective treatment that can cure AD, but the process of early-stage AD can slow down. Early and accurate detection is critical for treatment. In recent years, deep-learning-based approaches have achieved great success in Alzheimer's disease diagnosis. The main objective of this paper is to review some popular conventional machine learning methods used for the classification and prediction of AD using Magnetic Resonance Imaging (MRI). The methods reviewed in this paper include support vector machine (SVM), random forest (RF), convolutional neural network (CNN), autoencoder, deep learning, and transformer. This paper also reviews pervasively used feature extractors and different types of input forms of convolutional neural network. At last, this review discusses challenges such as class imbalance and data leakage. It also discusses the trade-offs and suggestions about pre-processing techniques, deep learning, conventional machine learning methods, new techniques, and input type selection. Frontiers Media S.A. 2023-02-06 /pmc/articles/PMC9939698/ /pubmed/36814932 http://dx.doi.org/10.3389/fncom.2023.1038636 Text en Copyright © 2023 Zhao, Chuah, Lai, Chow, Gochoo, Dhanalakshmi, Wang, Bao and Wu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Zhao, Zhen Chuah, Joon Huang Lai, Khin Wee Chow, Chee-Onn Gochoo, Munkhjargal Dhanalakshmi, Samiappan Wang, Na Bao, Wei Wu, Xiang Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review |
title | Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review |
title_full | Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review |
title_fullStr | Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review |
title_full_unstemmed | Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review |
title_short | Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review |
title_sort | conventional machine learning and deep learning in alzheimer's disease diagnosis using neuroimaging: a review |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9939698/ https://www.ncbi.nlm.nih.gov/pubmed/36814932 http://dx.doi.org/10.3389/fncom.2023.1038636 |
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