Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61408
Title: Deep learning to detect Alzheimer's disease from neuroimaging: A systematic literature review
Contributor(s): Ebrahimighahnavieh, Amir (author); Luo, Suhuai (author); Chiong, Raymond  (author)orcid 
Publication Date: 2020-04
DOI: 10.1016/j.cmpb.2019.105242
Handle Link: https://hdl.handle.net/1959.11/61408
Abstract: 

Alzheimer's Disease (AD) is one of the leading causes of death in developed countries. From a research point of view, impressive results have been reported using computer-aided algorithms, but clinically no practical diagnostic method is available. In recent years, deep models have become popular, especially in dealing with images. Since 2013, deep learning has begun to gain considerable attention in AD detection research, with the number of published papers in this area increasing drastically since 2017. Deep models have been reported to be more accurate for AD detection compared to general machine learning techniques. Nevertheless, AD detection is still challenging, and for classification, it requires a highly discriminative feature representation to separate similar brain patterns. This paper reviews the current state of AD detection using deep learning. Through a systematic literature review of over 100 articles, we set out the most recent findings and trends. Specifically, we review useful biomarkers and features (personal information, genetic data, and brain scans), the necessary pre-processing steps, and different ways of dealing with neuroimaging data originating from single-modality and multi-modality studies. Deep models and their performance are described in detail. Although deep learning has achieved notable performance in detecting AD, there are several limitations, especially regarding the availability of datasets and training procedures.

Publication Type: Journal Article
Source of Publication: Computer Methods and Programs in Biomedicine, v.187, p. 1-22
Publisher: Elsevier Ireland Ltd
Place of Publication: Ireland
ISSN: 1872-7565
0169-2607
Fields of Research (FoR) 2020: 4602 Artificial intelligence
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

Files in This Item:
1 files
File SizeFormat 
Show full item record

SCOPUSTM   
Citations

231
checked on Oct 26, 2024
Google Media

Google ScholarTM

Check

Altmetric


Items in Research UNE are protected by copyright, with all rights reserved, unless otherwise indicated.