A Review on Computer Aided Diagnosis of Acute Brain Stroke.

Inamdar, MA; Raghavendra, U; Gudigar, A; Chakole, Y; Hegde, A; Menon, GR; Barua, P; Palmer, EE; Cheong, KH; Chan, WY; Ciaccio, EJ; Acharya, UR

A Review on Computer Aided Diagnosis of Acute Brain Stroke.

Inamdar, MA Raghavendra, U Gudigar, A Chakole, Y Hegde, A Menon, GR Barua, P

Palmer, EE Cheong, KH Chan, WY Ciaccio, EJ Acharya, UR

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Sensors (Basel), 2021, 21, (24)
Issue Date:: 2021-12-20

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Field	Value	Language
dc.contributor.author	Inamdar, MA
dc.contributor.author	Raghavendra, U
dc.contributor.author	Gudigar, A
dc.contributor.author	Chakole, Y
dc.contributor.author	Hegde, A
dc.contributor.author	Menon, GR
dc.contributor.author	Barua, P https://orcid.org/0000-0001-5117-8333
dc.contributor.author	Palmer, EE
dc.contributor.author	Cheong, KH
dc.contributor.author	Chan, WY
dc.contributor.author	Ciaccio, EJ
dc.contributor.author	Acharya, UR
dc.date.accessioned	2022-02-26T21:00:48Z
dc.date.available	2021-12-09
dc.date.available	2022-02-26T21:00:48Z
dc.date.issued	2021-12-20
dc.identifier.citation	Sensors (Basel), 2021, 21, (24)
dc.identifier.issn	1424-8220
dc.identifier.issn	1424-8220
dc.identifier.uri	http://hdl.handle.net/10453/154885
dc.description.abstract	Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas.
dc.format	Electronic
dc.language	eng
dc.publisher	MDPI
dc.relation.ispartof	Sensors (Basel)
dc.relation.isbasedon	10.3390/s21248507
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Analytical Chemistry
dc.subject.mesh	Brain
dc.subject.mesh	Computers
dc.subject.mesh	Diagnosis, Computer-Assisted
dc.subject.mesh	Humans
dc.subject.mesh	Prospective Studies
dc.subject.mesh	Stroke
dc.subject.mesh	Brain
dc.subject.mesh	Humans
dc.subject.mesh	Diagnosis, Computer-Assisted
dc.subject.mesh	Prospective Studies
dc.subject.mesh	Computers
dc.subject.mesh	Stroke
dc.title	A Review on Computer Aided Diagnosis of Acute Brain Stroke.
dc.type	Journal Article
utslib.citation.volume	21
utslib.location.activity	Switzerland
utslib.for	0301 Analytical Chemistry
utslib.for	0502 Environmental Science and Management
utslib.for	0602 Ecology
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
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/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	open_access	*
dc.date.updated	2022-02-26T21:00:36Z
pubs.issue	24
pubs.publication-status	Published online
pubs.volume	21
utslib.citation.issue	24

Abstract:

Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas.

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http://hdl.handle.net/10453/154885