Deep Learning Techniques for Early Detection of Alzheimer’s Disease: A Review

Abstract

Alzheimer's disease (AD) is the most prevalent kind of dementia illness that can significantly impair a person's capability to carry out everyday tasks. According to findings, AD may be the third provoking reason of mortality among older adults, behind cancer and heart disease. Individuals at risk of acquiring AD must be identified before treatment strategies may be tested. The study's goal is to give a thorough examination of tissue structures using segmented MRI, which will lead to a more accurately labeling of certain brain illnesses. Several complicated segmentation approaches for identify AD have been developed. DL algorithms for brain structure segmentation and AD categorization have gotten a lot of attention since they can deliver accurate findings over a huge amount of data. As a result, DL approaches are increasingly favored over cutting-edge Machine Learning (ML) techniques. This study provides you with an overview of current trend deep learning-based segmentation algorithms for analyzing brain Magnetic Resonance Imaging for the treatment of AD. Finally, a conversation on the approaches' benefits and drawbacks, as well as future directives, was held, which may help researchers better comprehend present algorithms and methods in this field, and eventually design new and more successful algorithms.