Cascaded deep convolutional encoder-decoder neural networks for efficient liver tumor segmentation

dc.contributor.author	Budak, Umit
dc.contributor.author	Guo, Yanhui
dc.contributor.author	Tanyildizi, Erkan
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/20.500.12643/10049
dc.identifier.uri	https://doi.org/10.1016/j.mehy.2019.109431
dc.description.abstract	Liver and hepatic tumor segmentation remains a challenging problem in Computer Tomography (CT) images analysis due to its shape variation and vague boundary. The general hypothesis says that deep learning methods produce improved results on medical image
dc.language.iso	English
dc.publisher	Churchıll Lıvıngstone
dc.source	Medıcal Hypotheses
dc.title	Cascaded deep convolutional encoder-decoder neural networks for efficient liver tumor segmentation
dc.type	Article
dc.identifier.doi	10.1016/j.mehy.2019.109431
dc.identifier.wos	WOS:000510971500023
dc.identifier.volume	134

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