Early Diabetic Retinopathy Detection using Deep Learning

Abstract

Abstract: Typically, hazy and low contrast, Diabetic Retinopathy (DR) results from damage to the blood vessels inside the delicate tissue behind the eye (that is, the retina). Fundus images produced by fundus cameras are frequently imperfect. The risk of blindness in DR patients can be decreased with early detection and treatment. Finding and documenting diabetic retinopathy in people is a challenging and error-prone procedure because it is challenging to capture photographs with colour fundus photography. DR classes can be identified and categorised early by utilising machine learning techniques and a variety of screening criteria. These methods' accuracy, though, is below average. This shortcoming of these techniques can be remedied by using a different technique for the task such as deep learning. This technique uses image metadata to train a deep learning model and learn features using hundreds of classes in DR. This allows experts to create models that can classify invisible images into appropriate classes or levels of acceptable accuracy. This paper proposed a DenseNet201 model to classify fundus images into the correct severity class. We trained the model on 1914 images per class and used checkpoints to save the best weight automatically.