Diabetic Retinopathy Detection from Retinal Images

Abstract

Diabetic Retinopathy (DR) is an eye disease that can occur in people with diabetes causing permanent or temporary blindness, where the blood vessels of retina, a layer in rear interior eye and so called light sensitive part effected by high sugar damages nerves. As I mentioned above, DR can present in various ways; Some people with sick retinas experience blurred vision, while others have difficulty seeing colors or eye floaters. At first, DR might cause no symptoms or only mild vision problems. But it can lead to blindness if left undiagnosed and untreated. This study presents a pipeline for processing and analyzing retinal images. The pipeline consists of three primary steps: (i) pre-processing images, (ii) extracting features, and (iii) classifying the results. The initial step involves pre-processing the image using various modifications to improve its quality and standardize it. Gaussian filtering has been demonstrated to work fairly well in this situation for improving the contrast of the photos. Convolutional Neural Network (CNN) based pre-trained machine learning algorithm is used to speed up the diagnosis of the severity of DR using retinal images of patients. Convolution neural networks (CNNs), among the greatest neural network architectures for image processing applications, have been utilized in the second and third stages. Keywords-Gaussian filtering,Convolutional Neural Network,Retinalimages,Diabetic retinopathy,hemorrhage,, and exudates.