TY - JOUR
T1 - Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging
T2 - A review
AU - Zhao, Zhen
AU - Chuah, Joon Huang
AU - Lai, Khin Wee
AU - Chow, Chee Onn
AU - Gochoo, Munkhjargal
AU - Dhanalakshmi, Samiappan
AU - Wang, Na
AU - Bao, Wei
AU - Wu, Xiang
N1 - Publisher Copyright:
Copyright © 2023 Zhao, Chuah, Lai, Chow, Gochoo, Dhanalakshmi, Wang, Bao and Wu.
PY - 2023/2/6
Y1 - 2023/2/6
N2 - 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.
AB - 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.
KW - Alzheimer's disease
KW - Magnetic Resonance Imaging
KW - classification
KW - convolutional neural network
KW - deep learning
KW - machine learning
KW - neuroimaging
KW - transformer
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U2 - 10.3389/fncom.2023.1038636
DO - 10.3389/fncom.2023.1038636
M3 - Review article
AN - SCOPUS:85148488557
SN - 1662-5188
VL - 17
JO - Frontiers in Computational Neuroscience
JF - Frontiers in Computational Neuroscience
M1 - 1038636
ER -