Detection of Alzheimer's disease using deep learning models: A systematic literature review

Abstract

Alzheimer’s disease (AD) is a progressive neurological disease considered the most common form of late-stage dementia. Usually, AD leads to a reduction in brain volume, impacting various functions. This article comprehensively analyzes the AD context in fivefold main topics. Firstly, it reviews the main imaging techniques used in diagnosing AD disease. Secondly, it explores the most proposed deep learning (DL) algorithms for detecting the disease. Thirdly, the article investigates the commonly used datasets to develop DL techniques. Fourthly, we conducted a systematic review and selected 45 papers published in highly ranked publishers (Science Direct, IEEE, Springer, and MDPI). We analyzed them thoroughly by delving into the stages of AD diagnosis and emphasizing the role of preprocessing techniques. Lastly, the paper addresses the remaining practical implications and challenges in the AD context. Building on the analysis, this survey contributes to covering several aspects related to AD disease that have not been studied thoroughly.

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