Residual convolutional neural network for diabetic retinopathy

This research proposes a method to detect diabetic retinopathy automatically based on fundus photography evaluation. This automatic method will speed up diabetic retinopathy detection process especially in Indonesia which lack of ophthalmologist. Besides, the difference of doctor ability and experience may produce an inconsistent result. Thus, with this method, we hope automatic detection of diabetic retinopathy will speed up with a consistent result so blindness effect from diabetic retinopathy can be prevented as early as possible. Convolutional Neural Network (CNN) is one of neural network variant which can detect the pattern on an image very well. Residual CNN is one of CNN variant which can prevent accuracy degradation for a deep neural network. Therefore this inspire us to apply Residual CNN on diabetic retinopathy. This Residual Network can detect diabetic retinopathy with kappa score 0.51049.

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