Published Paper Details:

Diabetic Retinopathy is one of the major causes of blindness around the world. This disease affects those people who suffer from diabetes and mainly aged diabetes patients. Many hospitals around the world try their best to conduct research and prevent blindness caused due to diabetic retinopathy. Being able to detect diabetic retinopathy before it is too advanced is one of the major aims of this project. In this work, we will try to detect whether a patient has reached the stage of diabetic retinopathy or not. In accordance with this, we will use deep learning neural networks to classify the images of eyes of patients. Medical imaging analysis using deep learning and neural networks is gaining popularity because of its effectiveness and positive results. In this project, we will train a neural network on the retina images taken using fundus photography. After the training the neural network will be able to classify whether a patient has diabetic retinopathy or not and to what extent. The successful completion of this project may help many medical practitioners carry out the eye examination of diabetes patients in an easy and effective manner. The detection of diabetic retinopathy will also become more accurate and less time consuming. This will lead to better treatment and also a reduction in cost as physical examination of the retina images by technicians will be minimal.