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REVIEW article

Front. Comput. Sci.
Sec. Computer Vision
Volume 6 - 2024 | doi: 10.3389/fcomp.2024.1404494

A Comprehensive Review on Early Detection of Alzheimer's Disease using Various Deep Learning Techniques

Provisionally accepted
Nagarajan I Nagarajan I Lakshmi Priya G G Lakshmi Priya G G *
  • VIT University, Vellore, Tamil Nadu, India

The final, formatted version of the article will be published soon.

    Alzheimer's disease (AD) is a type of brain disease that makes it hard for someone to perform daily tasks. Early diagnosis and classification of the condition are thought to be essential study areas due to the speedy progression of the disease in people living with dementia and the absence of precise diagnostic procedures. One of the main aims of the researchers is to correctly identify the early stages of AD so that the disease can be prevented or significantly reduced.The main objective of the current review is to thoroughly examine the most recent work on early AD detection and classification using the deep learning (DL) approach. This paper examined the purpose of an early diagnosis of AD, the various neuroimaging modalities, the pre-processing methods that were employed, the maintenance of data, the deep learning used in classifying AD from magnetic resonance imaging (MRI) images, the publicly available datasets, and the data that were fed into the deep models. A comparative analysis of different classification methods using DL techniques is performed. Further, the paper discussed the challenges involved in AD detection.

    Keywords: Alzheimer's disease, Challenges, MRI, deep learning, pre-processing, Feature extraction and classification

    Received: 21 Mar 2024; Accepted: 20 Dec 2024.

    Copyright: © 2024 I and G G. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Lakshmi Priya G G, VIT University, Vellore, 632 014, Tamil Nadu, India

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.