Advanced Deep Learning Approaches for Alzheimer's Disease

Abstract

In the field of computer vision, in particular, deep learning, a cutting-edge method for machine learning, has surpassed conventional machine learning in its ability to detect intricate structures in complicated, high-dimensional data. With the advent of large-scale multi-modal neuroimaging data made possible by the fast development of neuroimaging techniques, there has been a surge of interest in using deep learning for the automated classification and early detection of Alzheimer's disease (AD). This chapter presents the results of a literature review on the topic of Alzheimer's disease diagnostic classification using deep learning methods and neuroimaging data. The authors also summarize the data sources, processing steps, training protocols, and evaluation methods to help future deep-learning studies on Alzheimer's disease researchers. There are limitations to deep learning, despite its promising performance across studies and tasks.