A Deep Neural Network for Detecting the Severity Level of Diabetic Retinopathy from Retinography Images

We propose a deep neural network for detecting the severity levels of diabetic retinopathy disease by analyzing high-resolution retinography images. The architecture of this network is heuristically constructed starting with the simplest possible structure with no hidden layers, and progressively improving it by adding three types of hidden layers: fully connected, convolutional, and pooling layers. The training, validation, and generalization test of this architecture were performed on a real-world, open-access, and BSD-licensed dataset containing 3662 high-resolution color images, 72% of which were used to train the model while 20% were reserved for the validation process, and 8% for the generalization test on unseen images. The experimental results show that the proposed network yields excellent results in detecting the presence or the absence of the disease, and very good and promising results in distinguishing between its different levels of severity.