A Brief Review of Deep Learning Algorithms for Alzheimer's disease Detection

Abstract

Accurate diagnosis of Alzheimer's disease (AD) is crucial, especially in its early stages, as it enables timely intervention and preventive measures before irreversible brain damage occurs. While earlier studies have used computer-based methods for AD diagnosis, many of these methods are limited by inherent observations. Early-stage AD can be diagnosed but not predicted, as predictions are only applicable before the disease becomes manifest. Deep Learning (DL) has emerged as a promising technique for early AD diagnosis. In this review, we explore relevant literature and discuss how DL can aid researchers in diagnosing the disease in its early phases.