Deep CNN based decision support system for detection and assessing the stage of diabetic retinopathy

The diabetic retinopathy is a disease caused by long-standing diabetes. Lack of effective treatment can lead to vision impairment and even irreversible blindness. The disease can be diagnosed by examining digital color fundus photographs of retina. In this paper we propose deep learning approach to automated diabetic retinopathy screening. Deep convolutional neural networks (CNN) — the most popular kind of deep learning algorithms — enjoyed great success in the field of image analysis and recognition. Therefore, we leverage CNN networks to diagnose the diabetic retinopathy and its current stage, based on analysis of the photographs of retina. The utilized models were trained using dataset containing over 88000 retina photographs, labeled by specialist clinicians. To enhance the performance of the system, we proposed a special class coding technique that enabled to include the information about value of difference between predicted score and target score into the objective function being minimized during the neural networks training. To evaluate classification ability of employed models we used standard accuracy metrics and quadratic weighted Kappa score that is calculated between the predicted scores and scores provided in the dataset. The best tested model achieved an accuracy of about 82% in detecting the retinopathy and 51% in assessing its stage. Moreover, system obtained decent Kappa score equal 0.776. Achieved results showed that deep learning algorithms can be successfully employed to solve this very hard to analyze problem.