Deep-Learning-Based Diagnosis and Prognosis of Alzheimer's Disease: A Comprehensive Review

Sharma, R; Goel, T; Tanveer, M; Lin, CT; Murugan, R

Deep-Learning-Based Diagnosis and Prognosis of Alzheimer's Disease: A Comprehensive Review

Sharma, R Goel, T Tanveer, M Lin, CT Murugan, R

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Cognitive and Developmental Systems, 2023, 15, (3), pp. 1123-1138
Issue Date:: 2023-09-01

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Full metadata record

Field	Value	Language
dc.contributor.author	Sharma, R
dc.contributor.author	Goel, T
dc.contributor.author	Tanveer, M
dc.contributor.author	Lin, CT
dc.contributor.author	Murugan, R
dc.date.accessioned	2024-03-05T01:19:10Z
dc.date.available	2024-03-05T01:19:10Z
dc.date.issued	2023-09-01
dc.identifier.citation	IEEE Transactions on Cognitive and Developmental Systems, 2023, 15, (3), pp. 1123-1138
dc.identifier.issn	2379-8920
dc.identifier.issn	2379-8939
dc.identifier.uri	http://hdl.handle.net/10453/176108
dc.description.abstract	Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common cause of Dementia. Neuroimaging analyses, such as T1 weighted magnetic resonance imaging, positron emission tomography, and the deep learning (DL) approaches have attracted researchers for automated AD diagnosis in the early stages. Therefore, a review is required to understand DL algorithms to develop more efficient AD diagnosis methods. This article discusses a detailed review of automated early AD diagnosis using DL methods published from 2009 to 2022. The novelties of this article include: 1) introducing popular imaging modalities; 2) discussing early biomarkers for AD diagnosis using neuroimaging scans; 3) reviewing the popular online available data sets widely used; 4) systematically describing the various DL algorithms for accurate and early assessment of AD; 5) discussion on advantages and limitations of the DL-based model for AD diagnosis; and 6) provides an outlook toward future trends derived from our critical assessment.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Cognitive and Developmental Systems
dc.relation.isbasedon	10.1109/TCDS.2023.3254209
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	4007 Control engineering, mechatronics and robotics
dc.subject.classification	4611 Machine learning
dc.title	Deep-Learning-Based Diagnosis and Prognosis of Alzheimer's Disease: A Comprehensive Review
dc.type	Journal Article
utslib.citation.volume	15
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2025-09-01T00:00:00+1000Z
dc.date.updated	2024-03-05T01:19:03Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	15
utslib.citation.issue	3

Abstract:

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common cause of Dementia. Neuroimaging analyses, such as T1 weighted magnetic resonance imaging, positron emission tomography, and the deep learning (DL) approaches have attracted researchers for automated AD diagnosis in the early stages. Therefore, a review is required to understand DL algorithms to develop more efficient AD diagnosis methods. This article discusses a detailed review of automated early AD diagnosis using DL methods published from 2009 to 2022. The novelties of this article include: 1) introducing popular imaging modalities; 2) discussing early biomarkers for AD diagnosis using neuroimaging scans; 3) reviewing the popular online available data sets widely used; 4) systematically describing the various DL algorithms for accurate and early assessment of AD; 5) discussion on advantages and limitations of the DL-based model for AD diagnosis; and 6) provides an outlook toward future trends derived from our critical assessment.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/176108