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Automatic classification of diabetic retinopathy based on convolutional neural networks
Author(s): Xingming Zhang; Wanwan Zhang; Mingchao Fang; Jiale Xue; Lifeng Wu
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Paper Abstract

In this paper, we propose a novel classification algorithm based on convolutional neural networks (CNNs) to diagnose the severity of diabetic retinopathy (DR). We adopt a series of preprocessing operations to improve the quality of dataset. In addition, data augmentation is implemented on the training data to tackle the problem of imbalanced dataset. We design a CNNs model named DR-Net with a new Adaptive Cross-Entropy Loss, which emphasizes the difference of the penalty when training data are misclassified into different intervals. We train DR-Net on the publicly available Kaggle dataset. Experimental results show that our DR-Net achieves an accuracy of 0.821 and a kappa score of 0.663 on 3338 testing images.

Paper Details

Date Published: 29 October 2018
PDF: 6 pages
Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 1083608 (29 October 2018); doi: 10.1117/12.2503883
Show Author Affiliations
Xingming Zhang, ZheJiang Dahua Technology Co., Ltd. (China)
Wanwan Zhang, ZheJiang Dahua Technology Co., Ltd. (China)
Mingchao Fang, ZheJiang Dahua Technology Co., Ltd. (China)
Jiale Xue, ZheJiang Dahua Technology Co., Ltd. (China)
Lifeng Wu, ZheJiang Dahua Technology Co., Ltd. (China)


Published in SPIE Proceedings Vol. 10836:
2018 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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