A retinal vessel detection approach using convolution neural network with reinforcement sample learning strategy

dc.contributor.author	Guo, Yanhui
dc.contributor.author	Budak, Umit
dc.contributor.author	Vespa, Lucas J.
dc.contributor.author	Khorasani, Elham
dc.contributor.author	Sengur, Abdulkadir
dc.date.accessioned	16/12/21 12:07
dc.date.available	16/12/21 12:07
dc.date.issued	2018
dc.identifier.issn	0263-2241
dc.identifier.uri	http://dspace.beu.edu.tr:8080/xmlui/handle/20.500.12643/10225
dc.identifier.uri	https://doi.org/10.1016/j.measurement.2018.05.003
dc.description.abstract	Computer-aided detection (CAD) provides an efficient way to assist doctors to interpret fundus images. In a CAD system, retinal vessel (RV) detection is an important step to identify the retinal disease regions automatically and accurately. However, RV de
dc.language.iso	English
dc.publisher	Elsevıer Scı Ltd
dc.source	Measurement
dc.title	A retinal vessel detection approach using convolution neural network with reinforcement sample learning strategy
dc.type	Article
dc.identifier.startpage	586
dc.identifier.endpage	591
dc.identifier.doi	10.1016/j.measurement.2018.05.003
dc.identifier.wos	WOS:000436642500065
dc.identifier.volume	125

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