Diabetic retinopathy is a complication of diabetes that targets the eyes by damaging the retinal blood vessels. Primarily occurs when the blood sugar level is unmanageable. Therefore, the person with diabetes mellitus is always at a high risk of acquiring this disease. The present work considers a deep learning methodology specifically a Densely Connected Convolution Network iNCEPTION-v300, which is applied for the early detection of diabetic retinopathy. It classifies the funds images based on its severity levels as No DR, and Yes DR. The datasets that are taken into consideration are Diabetic Retinopathy Detection 2015 and Aptos 2019 Blindness Detection which are both obtained from Kaggle. Our proposed model achieved 88.1% of accuracy. The main aim of this work is to develop a robust system for detecting DR automatically.
₹10000 (INR)
NON IEEE -2022