Machine learning techniques for the diagnosis of alzheimer's disease: A review

Tanveer, M; Richhariya, B; Khan, RU; Rashid, AH; Khanna, P; Prasad, M; Lin, CT

Machine learning techniques for the diagnosis of alzheimer's disease: A review

Tanveer, M Richhariya, B Khan, RU Rashid, AH Khanna, P Prasad, M Lin, CT

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Journal Article
Citation:: ACM Transactions on Multimedia Computing, Communications and Applications, 2020, 16, (1s), pp. 1-35
Issue Date:: 2020-04-01

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Field	Value	Language
dc.contributor.author	Tanveer, M
dc.contributor.author	Richhariya, B
dc.contributor.author	Khan, RU
dc.contributor.author	Rashid, AH
dc.contributor.author	Khanna, P
dc.contributor.author	Prasad, M
dc.contributor.author	Lin, CT
dc.date.accessioned	2020-07-22T22:39:14Z
dc.date.available	2020-07-22T22:39:14Z
dc.date.issued	2020-04-01
dc.identifier.citation	ACM Transactions on Multimedia Computing, Communications and Applications, 2020, 16, (1s), pp. 1-35
dc.identifier.issn	1551-6857
dc.identifier.issn	1551-6865
dc.identifier.uri	http://hdl.handle.net/10453/141836
dc.description.abstract	© 2020 ACM. Alzheimer's disease is an incurable neurodegenerative disease primarily affecting the elderly population. Efficient automated techniques are needed for early diagnosis of Alzheimer's. Many novel approaches are proposed by researchers for classification of Alzheimer's disease. However, to develop more efficient learning techniques, better understanding of the work done on Alzheimer's is needed. Here, we provide a review on 165 papers from 2005 to 2019, using various feature extraction and machine learning techniques. The machine learning techniques are surveyed under three main categories: support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) and ensemble methods. We present a detailed review on these three approaches for Alzheimer's with possible future directions.
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation.ispartof	ACM Transactions on Multimedia Computing, Communications and Applications
dc.relation.isbasedon	10.1145/3344998
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0803 Computer Software, 0805 Distributed Computing, 0806 Information Systems
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Machine learning techniques for the diagnosis of alzheimer's disease: A review
dc.type	Journal Article
utslib.citation.volume	16
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-07-22T22:39:09Z
pubs.issue	1s
pubs.publication-status	Published
pubs.volume	16
utslib.citation.issue	1s

Abstract:

© 2020 ACM. Alzheimer's disease is an incurable neurodegenerative disease primarily affecting the elderly population. Efficient automated techniques are needed for early diagnosis of Alzheimer's. Many novel approaches are proposed by researchers for classification of Alzheimer's disease. However, to develop more efficient learning techniques, better understanding of the work done on Alzheimer's is needed. Here, we provide a review on 165 papers from 2005 to 2019, using various feature extraction and machine learning techniques. The machine learning techniques are surveyed under three main categories: support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) and ensemble methods. We present a detailed review on these three approaches for Alzheimer's with possible future directions.

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http://hdl.handle.net/10453/141836