Convolutional Neural Networks for Branch Retinal Vein Occlusion recognition?

Runqi Zhao, Zenghai Chen, Zheru Chi

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

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.
Original languageEnglish
Title of host publication2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics
PublisherIEEE
Pages1633-1636
Number of pages4
ISBN (Electronic)9781467391047
DOIs
Publication statusPublished - 28 Sep 2015
Event2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics - Yunnan, China
Duration: 8 Aug 201510 Aug 2015

Conference

Conference2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics
CountryChina
CityYunnan
Period8/08/1510/08/15

Keywords

  • Branch Retinal Vein Occlusion
  • Convolutional Neural Networks
  • Feature Extraction

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Human-Computer Interaction
  • Computational Theory and Mathematics
  • Control and Systems Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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