A Deep Analysis for quick Diagnosis of Diabetic Retinopathy

Diabetic Retinopathy (DR) is one of the most important causes for the presence of blindness in the recent days. Ophthalmologists need to diagnose the presence and severity of DR using the visual assessment of the retinal fundus images by manual examination. This process of manual diagnosis of DR is a very hectic and time consuming task. Due to the increasing rate of diabetic retinopathy patients in the world, the number of color fundus images generated has increased rapidly. Because of this large number, there is a huge delay in diagnosing the early symptoms of DR and providing timely good treatment. Therefore, to address this issue, there is a need to develop an automated framework of Diabetic Retinopathy diagnosis. Hence, in this analysis we have proposed a Deep framework for DR diagnosis. The analysis uses a modified version of standard Convolutional Neural Network (CNN) for solving DR fundus image classification problems. The proposed analysis efficiently reports whether the person has DR or not and if present, and indicates the severity of the disease. The analysis implemented helps in giving timely treatment to the patients irrespective of geographical and economic constraints.