Diabetic Retinopathy Classification with a Light Convolutional Neural Network

Abstract

The number of diabetic patients is increasing rapidly every year all around the world, and the worst fact is that these patients suffer from a wide range of physical conditions directly associated with long-term diabetes. Diabetic Retinopathy (DR) is a perfect example which affects the eyes of more than 50% of all diabetes patients to some degree. Starting from blurred vision, the effects of DR can extend to permanent blindness; and in most of the cases, victims fail to report any early symptoms. The traditional detection process of DR involves a trained clinician who takes enhanced pictures of the retina and looks for the presence of lesions and vascular abnormalities within them, which by description is a time-consuming and error-prone procedure. Alternatively, we can employ machine learning techniques that will automate the detection process as well as provide fast and more importantly, reliable results. Using a deep learning technique this paper determines the presence and severity of DR in diabetic individuals by analyzing the pictures of their retina. The CNN-based models are potent enough to carry out their tasks with accuracy up to 89.07%, even when the images are captured or provided in very low resolutions.