A survey of Alzheimer’s disease diagnosis using deep learning approaches

Abstract

The dementia with the highest prevalence is Alzheimer’s Disease (AD), which can cause a nervous brain syndrome that impairs daily functioning as well as causes gradual remembrance loss by harming brain cells. This fatal condition is exceptional in its field. Early identification of AD is important due to the disease’s global prevalence and evolving threat. Early detection holds promise because it can help predict the health of many people who may be encountered in the future. Therefore, by evaluating the disease’s effects using Magnetic Resonance Imaging (MRI) scans, we may use Artificial Intelligence (AI) technology to categorize AD patients and determine whether or not they will eventually develop the fatal condition. In the area of deep learning methods and analysis, this paper presents essential knowledge and cutting-edge deep learning techniques. The goals of the paper are to advance the knowledge and implementation of medical image processing methods for AD. The paper aims to advance the body of knowledge and promote the creation of efficient and standardised ways in the field by discussing the pertinent techniques and putting recognised recommendations into practise.