Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/67524
DC FieldValueLanguage
dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorZhao, R-
dc.creatorChen, Z-
dc.creatorChi, Z-
dc.date.accessioned2017-07-27T08:33:37Z-
dc.date.available2017-07-27T08:33:37Z-
dc.identifier.isbn978-1-4673-9104-7 (electronic)-
dc.identifier.isbn978-1-4673-9103-0 (USB)-
dc.identifier.isbn978-1-4673-9105-4 (print on demand(PoD))-
dc.identifier.urihttp://hdl.handle.net/10397/67524-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.subjectFeature extractionen_US
dc.subjectConvolutional neural networksen_US
dc.subjectBranch retinal vein occlusionen_US
dc.titleConvolutional neural networks for branch retinal vein occlusion recognition?en_US
dc.typeConference Paperen_US
dc.identifier.spage1633-
dc.identifier.epage1636-
dc.identifier.doi10.1109/ICInfA.2015.7279547-
dcterms.abstractBranch Retinal Vein Occlusion (BRVO) is one of the most common retinal diseases that could impair people's vision seriously if it is not timely diagnosed and treated. It would save a lot of time and money for both medical institutions and patients if BRVO could be well recognized automatically. In this paper, we propose to exploit Convolutional Neural Networks (CNN) for BRVO recognition. We propose patch-based method and image-based voting method to implement the recognition. As it could learn abstract and useful features, CNN can achieve a high recognition accuracy. The accuracy of CNN is over 97%. Experimental results demonstrate the efficiency of our proposed CNN based methods for BRVO recognition.-
dcterms.bibliographicCitation2015 IEEE International Conference on Information and Automation, Lijiang, China, 8-10 Aug 2015-
dcterms.issued2015-
dc.relation.conferenceIEEE International Conference on Information and Automation-
dc.identifier.rosgroupid2015005323-
dc.description.ros2015-2016 > Academic research: refereed > Refereed conference paper-
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