Diabetic Retinopathy Using Deep Learning

Abstract

 Diabetic retinopathy is one of the most threatening complications of diabetes that leads to permanent blindness if left untreated. One of the essential challenges is early detection, which is very important for treatment success. Unfortunately, the exact identification of the diabetic retinopathy stage is notoriously tricky and requires expert human interpretation of fundus images. Simplification of the detection step is crucial and can help millions of people. In this project we propose an automatic deep-learning-based method for stage detection of diabetic retinopathy by single photography of the human fundus. Convolutional neural networks (CNN) have been successfully applied in many adjacent subjects, and for diagnosis of diabetic retinopathy itself. However, the high cost of big labeled datasets, as well as inconsistency between different doctors, impede the performance of these methods. In this project we propose an automatic deep-learning-based method for stage detection of diabetic retinopathy by single photography of the human fundus. In this system, we analyzed diabetes detectability from retinal images in the Diabetic Retinopathy Database - Calibration Level Raw pixel intensities of extracted patches served directly as inputs into the following classifiers: CNN.