A Deep Convolutional Neural Network For Diagnosis of Diabetic Retinopathy

Diabetic Retinopathy (DR) diagnosis is a time consuming and complex task, in addition to requiring experienced doctors. In this study, an advanced Convolution Neural Network (CNN) and data augmentation were successfully used to predict the stages of diabetic retinopathy. Specifically, we modified and trained this network with the publicly available Kaggle dataset and obtained promising results. On the validation of 1452 images using this classifier, an accuracy of 82%, average sensitivity of 80% over the three categories and average specificity of 91% over the three categories could be achieved, indicating the feasibility of our CCN model for the identification of different stages of the diabetic retinopathy. It implies the great potential of this artificial intelligent method for the diagnosis of diabetic retinopathy, especially for the early diagnosis for patients in the future.