Analysis of Adversarial based Augmentation for Diabetic Retinopathy Disease Grading

Diabetic Retinopathy(DR) is one of the diseases, which is caused by damage to blood vessels of the light sensitive tissue of the eyes. DR is graded into five different levels; normal, mild, moderate, severe and proliferative. DR diagnosis demands more time on detection from fundus images. An accurate automatic model requires sufficient data for training which is unavailable. The open-source DR datasets are highly imbalanced between the levels of DR and it is also not easy to collect more data for proliferative cases. The synthetic generation of data for such highly imbalanced classes in a dataset provides better results on classification. In this paper, an analysis of classification results for the same is carried out with augmented proliferative class (highly imbalanced class) in the EYEPACS dataset using a generative adversarial network (GAN). We generated highly diverse images for proliferative cases without any constraints. The generated proliferative images do not influence other class images and have also improved the classification results obtained by the model, over that which was trained without synthetic generation. The results obtained before and after augmentation by the proposed generative based model is compared over various model attributes.