Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review

Zhao, Zhen and Chuah, Joon Huang and Lai, Khin Wee and Chow, Chee-Onn and Gochoo, Munkhjargal and Dhanalakshmi, Samiappan and Wang, Na and Bao, Wei and Wu, Xiang (2023) Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review. Frontiers in Computational Neuroscience, 17. DOI

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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.

Item Type: Article
Funders: None
Uncontrolled Keywords: Alzheimer's disease; Machine learning; Deep learning; Convolutional neural network; Transformer; Classification; Neuroimaging; Magnetic Resonance Imaging
Subjects: R Medicine > RC Internal medicine
Divisions: Faculty of Engineering > Biomedical Engineering Department
Faculty of Engineering > Department of Electrical Engineering
Depositing User: Ms Zaharah Ramly
Date Deposited: 14 Nov 2023 08:15
Last Modified: 03 Dec 2023 02:14

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