Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/67524
Title: Convolutional neural networks for branch retinal vein occlusion recognition?
Authors: Zhao, R
Chen, Z
Chi, Z 
Keywords: Feature extraction
Convolutional neural networks
Branch retinal vein occlusion
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: 2015 IEEE International Conference on Information and Automation, Lijiang, China, 8-10 Aug 2015 How to cite?
Abstract: Branch 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.
URI: http://hdl.handle.net/10397/67524
ISBN: 978-1-4673-9104-7 (electronic)
978-1-4673-9103-0 (USB)
978-1-4673-9105-4 (print on demand(PoD))
DOI: 10.1109/ICInfA.2015.7279547
Appears in Collections:Conference Paper

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