Aggregated residual transformation network for multistage classification in diabetic retinopathy (original) (raw)

Diabetic Retinopathy is a retinal abnormality which is characterized by progressive damage to the retina, eventually leading to irreversible blindness. In this paper, we propose an aggregated residual transformation-based model for automatic multistage classification of diabetic retinopathy. The proposed model obtains 99.68% overall classification accuracy, 99.68% sensitivity, 99.89% specificity and 99.68% precision without overfitting on the MESSIDOR dataset. Further, the model obtains an accuracy of 99.89% for stage 0, 99.89% for stage 1, 99.68% for stage 2 and 99.89% for stage 3 of diabetic retinopathy. In comparison to residual network, the model shows an overall accuracy gain of 0.52%. The model also ensures an overall improvement of more than 6% in accuracy, 1.2% in sensitivity and 2.43 % in specificity when compared to best results reported in the literature. The proposed work outperforms the existing methods and achieves state-of-the-art results for the multistage classification of diabetic retinopathy.