A retinal vessel detection approach using convolution neural network

dc.contributor.author	Şengür, A.
dc.contributor.author	Guo, Y.
dc.contributor.author	Budak, U.
dc.contributor.author	Vespa, L.J.
dc.date.accessioned	2021-12-16T10:12:14Z
dc.date.available	2021-12-16T10:12:14Z
dc.date.issued	2017
dc.identifier.isbn	9.78154E+12
dc.identifier.uri	https://doi.org/10.1109/IDAP.2017.8090331
dc.identifier.uri	http://dspace.beu.edu.tr:8080/xmlui/handle/20.500.12643/13067
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	Institute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof	2017 International Artificial Intelligence and Data Processing Symposium, IDAP 2017
dc.source	IDAP 2017 - International Artificial Intelligence and Data Processing Symposium
dc.title	A retinal vessel detection approach using convolution neural network
dc.type	Conference Paper
dc.identifier.doi	10.1109/IDAP.2017.8090331
dc.identifier.scopus	2-s2.0-85039923290

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