Inception V3 and CNN Approach to Classify Diabetic Retinopathy Disease

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Abstract: Diabetic retinopathy (DR) is one of the disease which is unobservable to the people and it is also an underlying disease that causes eye-related disorders due to collective damage to small retinal blood vessels. As a result both eyes can be affected leading to partial or complete vision loss. Diabetic Retinopathy is linked with uncontrollable sugar level or diabetic level. To prevent the initial damage and permanent blindness, its early detection must be followed. Whereas, there are automated diagnostic systems are implied in early detection and diagnosis of severe eye complications by providing helping hand to the ophthalmologists. The proposed research achieved improvements to enhance the performance in terms of accuracy, loss and speedy detection. In continuation with the same, the research has been done by implying Convolution Neural Network (CNN) along with Inception Version 3 (V3) model which yield accuracy of 99.35% with 0.02 loss. The proposed model makes it more accurate due to less epochs i.e., 10 epochs. The proposed model has been supported with Diabetic Retinopathy Detection 2015 and Aptos 2019 Blindness Detection and they both were received from Kaggle so to develop our reliable approach for identifying different Diabetic Retinopathy