Detection of diabetic blindness with Deep-Learning

Diabetes is one of the significant causes of blindness, especially among aged adults. As diabetes increases, the vision starts to deteriorate, which is called as diabetic retinopathy. The longer a person has diabetes and the less the blood sugar is controlled, the higher chance the person has diabetic retinopathy. 7.7 million people of age 40 or more have diabetic retinopathy. More than 90% of the cases causing vision loss can be avoided if detected early. Here, we use a deep-learning approach to automatically classify the fundus images into normal, mild, moderate, severe, and proliferative diabetic retinopathy. The dataset is collected from Aravind Eye Care System, and comprises of more than three thousand images. The images in the dataset are captured under diverse illumination conditions. We use a densely connected convolutional neural network architecture for the classification and detection of the severity level. Various pre-processing strategies are applied using OpenCV and Keras library to remove the noise from the image dataset. The model performs training for several epochs, and the hyperparameters are tuned to maximize the performance. Experimental results show that the model can successfully detect the severity of diabetic retinopathy.