A survey on recent computer-aided diagnosis for detecting diabetic retinopathy

Priyadharsini, C; Kannan R., J; Hussain, FK

A survey on recent computer-aided diagnosis for detecting diabetic retinopathy

Priyadharsini, C Kannan R., J Hussain, FK

Permalink

Publisher:: Wiley
Publication Type:: Chapter
Citation:: Tele-Healthcare: Applications of Artificial Intelligence and Soft Computing Techniques, 2022, pp. 27-58
Issue Date:: 2022-07-15

Closed Access

	Filename	Description	Size
	20906908_10940552200005671.pdf	Published version	2.26 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Priyadharsini, C
dc.contributor.author	Kannan R., J
dc.contributor.author	Hussain, FK
dc.date.accessioned	2023-06-20T20:33:12Z
dc.date.available	2023-06-20T20:33:12Z
dc.date.issued	2022-07-15
dc.identifier.citation	Tele-Healthcare: Applications of Artificial Intelligence and Soft Computing Techniques, 2022, pp. 27-58
dc.identifier.isbn	9781119841937
dc.identifier.uri	http://hdl.handle.net/10453/170805
dc.description.abstract	Diabetic retinopathy (DR) is a complication that causes changes in blood vessels present in the retina, which leads to vision loss at later stages, and has no or lesser symptoms during the initial stages. Diabetic patients present around the globe are prone to DR, and it is one of the leading causes of vision loss. Various intelligent systems are continuously evolving to assist ophthalmologists in identifying the retinal abnormalities. In this research, recent computer-aided diagnosis (CAD) systems are analyzed based on the nature of data, recent approaches taken in classifying DR, performance comparison using statistical parameters, existing limitations, and challenges. The comparison results indicate that developing a precise CAD system in identifying DR is still an open research problem. The objective of this research is to survey recent research works, identify the gap, and provide useful insights that would help researchers working in this field to develop more efficient CAD systems.
dc.language	en
dc.publisher	Wiley
dc.relation.ispartof	Tele-Healthcare: Applications of Artificial Intelligence and Soft Computing Techniques
dc.relation.isbasedon	10.1002/9781119841937.ch2
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A survey on recent computer-aided diagnosis for detecting diabetic retinopathy
dc.type	Chapter
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	closed_access	*
dc.date.updated	2023-06-20T20:33:10Z
pubs.publication-status	Published

Abstract:

Diabetic retinopathy (DR) is a complication that causes changes in blood vessels present in the retina, which leads to vision loss at later stages, and has no or lesser symptoms during the initial stages. Diabetic patients present around the globe are prone to DR, and it is one of the leading causes of vision loss. Various intelligent systems are continuously evolving to assist ophthalmologists in identifying the retinal abnormalities. In this research, recent computer-aided diagnosis (CAD) systems are analyzed based on the nature of data, recent approaches taken in classifying DR, performance comparison using statistical parameters, existing limitations, and challenges. The comparison results indicate that developing a precise CAD system in identifying DR is still an open research problem. The objective of this research is to survey recent research works, identify the gap, and provide useful insights that would help researchers working in this field to develop more efficient CAD systems.

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

http://hdl.handle.net/10453/170805