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 language | English |
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| Title of host publication | 2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics |
| Publisher | IEEE |
| Pages | 1633-1636 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781467391047 |
| DOIs | |
| Publication status | Published - 28 Sept 2015 |
| Event | 2015 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 2015 → 10 Aug 2015 |
Conference
| Conference | 2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics |
|---|---|
| Country/Territory | China |
| City | Yunnan |
| Period | 8/08/15 → 10/08/15 |
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