상위피인용논문
- 조회452
Joon Yul Choi1☯, Tae Keun Yoo2☯*, Jeong Gi Seo2, Jiyong Kwak2, Terry Taewoong Um3, Tyler Hyungtaek Rim2*
1Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea, 2Institute of Vision Research, Department of Ophthalmology, Yonsei University College of Medicine, Seoul, South Korea, 3Department of Electrical & Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada
☯These authors contributed equally to this work.
*Corresponding author.
Abstract
Deep learning emerges as a powerful tool for analyzing medical images. Retinal disease detection by using computer-aided diagnosis from fundus image has emerged as a new method. We applied deep learning convolutional neural network by using MatConvNet for an automated detection of multiple retinal diseases with fundus photographs involved in STructured Analysis of the REtina (STARE) database. Dataset was built by expanding data on 10 categories, including normal retina and nine retinal diseases. The optimal outcomes were acquired by using a random forest transfer learning based on VGG-19 architecture. The classification results depended greatly on the number of categories. As the number of categories increased, the performance of deep learning models was diminished. When all 10 categories were included, we obtained results with an accuracy of 30.5%, relative classifier information (RCI) of 0.052, and Cohen’s kappa of 0.224. Considering three integrated normal, background diabetic retinopathy, and dry age-related macular degeneration, the multi-categorical classifier showed accuracy of 72.8%, 0.283 RCI, and 0.577 kappa. In addition, several ensemble classifiers enhanced the multi-categorical classification performance. The transfer learning incorporated with ensemble classifier of clustering and voting approach presented the best performance with accuracy of 36.7%, 0.053 RCI, and 0.225 kappa in the 10 retinal diseases classification problem. First, due to the small size of datasets, the deep learning techniques in this study were ineffective to be applied in clinics where numerous patients suffering from various types of retinal disorders visit for diagnosis and treatment. Second, we found that the transfer learning incorporated with ensemble classifiers can improve the classification performance in order to detect multi-categorical retinal diseases. Further studies should confirm the effectiveness of algorithms with large datasets obtained from hospitals.
논문정보
- Research article
- 2017년 11월 (BRIC 등록일 2021-11-25)
- 국내(교신)+국외 연구진
- 바이오·의료융합 > 의료기기
- 120회 이상 인용된 논문
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