Publication

Abstract : Diabetic retinopathy (DR) is a complication of diabetes which is due to the impairment of blood vessels of photosensitive cells in the eyes. This complication results in loss of eyesight if not diagnosed in the early stages. There are five stages of diabetic retinopathy: No DR, mild, moderate, severe, and proliferative. DR detection by traditional methods consumes a lot of time. An automatic and precise model would require an adequate amount of data for training which is not available. Publicly available dataset is highly imbalanced for other classes apart from No DR, especially proliferative and severe classes. In this paper, a model is created in two stages. The first stage involves the generation of synthetic data points using Deep Convolutional Generative Adversarial Network (DCGAN). The synthetic data are for highly imbalanced classes namely severe and proliferative. The second phase involves the augmented data classification using a CNN architecture.