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 (Frontiers in Computational Neuroscience, 2023)
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 classical machine learning methods used for the classification and prediction of AD using neuroimaging. The methods reviewed in this paper include support vector machine, random forest, and convolutional neural network. This paper also reviews pervasively used feature extractors and different types of input forms of CNN. At last, this review suggests that researchers should investigate to use of the integration of convolutional neural networks and transformer-based deep learning methods, and vision-transformer-based pre-training methods in Alzheimer's disease detection and prediction in the future.