A novel microaneurysms detection approach based on convolutional neural networks with reinforcement sample learning algorithm

dc.contributor.author	Budak, Umit
dc.contributor.author	Sengur, Abdulkadir
dc.contributor.author	Guo, Yanhui
dc.contributor.author	Akbulut, Yaman
dc.date.accessioned	2021-12-16T09:07:22Z
dc.date.available	2021-12-16T09:07:22Z
dc.date.issued	2017
dc.identifier.issn	2047-2501
dc.identifier.uri	https://doi.org/10.1007/s13755-017-0034-9
dc.identifier.uri	http://dspace.beu.edu.tr:8080/xmlui/handle/20.500.12643/10339
dc.description.abstract	Microaneurysms (MAs) are known as early signs of diabetic-retinopathy which are called red lesions in color fundus images. Detection of MAs in fundus images needs highly skilled physicians or eye angiography. Eye angiography is an invasive and expensive p
dc.description.sponsorship	Scientific and Technological Research Council of TurkeyTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [TUBITAK-1512, 2150121]
dc.language.iso	English
dc.publisher	Bıomed Central Ltd
dc.rights	Green Published
dc.source	Health Informatıon Scıence And Systems
dc.title	A novel microaneurysms detection approach based on convolutional neural networks with reinforcement sample learning algorithm
dc.type	Article
dc.identifier.doi	10.1007/s13755-017-0034-9
dc.identifier.wos	WOS:000414469200002
dc.identifier.volume	5

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