Efficient Classification Of Diabetic Retinopathy Using Binary Cnn

Aim:

To detect the diabetic retinopathy disease in the earlier stage using Deep learning method

Synopsis:

         Diabetic Retinopathy is a disease that can lead to partial or complete blindness. Research shows that it contributes around 5 percent of the total cases of blindness. Usually it takes about two weeks for the diagnosis of disease; time and money both are wasted. The proposed system aims to eradicate the above problem. Convolutional Neural Network (CNNs) is widely used in pattern and image recognition problems as they have a number of advantages compared to other techniques. Aim of the project is to provide an automated, suitable and sophisticated approach using Convolutional Neural Network (CNN).

Proposed System:

      A model is proposed which uses CNN for the automated detection of Diabetic Retinopathy. The client on their first login has to register themselves on the Web Application. The web Application created by Flask. Once the user logins into the system he gets all the access for predicting the diabetic retinopathy by using the input image. After submitting the inputs, it’s move on to the trained model for comparison. Already trained model were trained by deep learning algorithms. So, we get accuracy results in this project using CNN.