Machine Learning and Deep Learning Approaches for detecting Alzheimer’s Disease (AD): A Review

Abstract

Alzheimer's disease (AD) is the most prevalent chronic disease among the elderly, with a high prevalence. In the treatment of AD, early diagnosis of the patient plays an important role because of severe damage to the brain later. Deep learning (DL) and Machine Learning (ML) have gained popularity and success in the field of medical imaging in recent years. It has become the dominant way of assessing medical pictures, and it has also sparked considerable interest in the diagnosis of Alzheimer's disease. The deep and machine models are more precise and efficient for AD detection than ordinary machine learning techniques. This study provides AD-related biomarkers and feature extraction methods, discusses the use of the machine and deep learning approaches in AD detection, and analyses and summarises AD detection methodologies and models. The results suggest that DL and ML technology performs well in detecting AD.