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29 October 2018 Automatic classification of diabetic retinopathy based on convolutional neural networks
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Proceedings Volume 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence; 1083608 (2018) https://doi.org/10.1117/12.2503883
Event: 2018 International Conference on Image, Video Processing and Artificial Intelligence, 2018, Shanghai, China
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.
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Xingming Zhang, Wanwan Zhang, Mingchao Fang, Jiale Xue, and Lifeng Wu "Automatic classification of diabetic retinopathy based on convolutional neural networks", Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 1083608 (29 October 2018); https://doi.org/10.1117/12.2503883
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