DCCMED-Net: Densely connected and concatenated multi Encoder-Decoder CNNs for retinal vessel extraction from fundus images

dc.contributor.author	Budak, Umit
dc.contributor.author	Comert, Zafer
dc.contributor.author	Cibuk, Musa
dc.contributor.author	Sengur, Abdulkadir
dc.date.accessioned	16/12/21 12:06
dc.date.available	16/12/21 12:06
dc.date.issued	2020
dc.identifier.issn	0306-9877
dc.identifier.uri	http://dspace.beu.edu.tr:8080/xmlui/handle/20.500.12643/10048
dc.identifier.uri	https://doi.org/10.1016/j.mehy.2019.109426
dc.description.abstract	Recent studies have shown that convolutional neural networks (CNNs) can be more accurate, efficient and even deeper on their training if they include direct connections from the layers close to the input to those close to the output in order to transfer a
dc.language.iso	English
dc.publisher	Churchıll Lıvıngstone
dc.source	Medıcal Hypotheses
dc.title	DCCMED-Net: Densely connected and concatenated multi Encoder-Decoder CNNs for retinal vessel extraction from fundus images
dc.type	Article
dc.identifier.doi	10.1016/j.mehy.2019.109426
dc.identifier.wos	WOS:000510971500007
dc.identifier.volume	134

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