Diabetic Retinopathy Prediction Using Machine Learning

Abstract

Diabetic retinopathy is among the notorious complications of diabetes and a leading cause of blindness among adults. Since prevention of vision loss in diabetic retinopathy is possible when the disease is detected early and interventions instituted, screening is highly essential for this disease. However, the process of diagnosing the manual images or studying the images of retinas is lengthy, and because of the connectivity of these interconnections, they blur in certain cases. This research aims to provide solutions to the preceding challenges through the development of a web application that can have the ability to diagnose diabetic retinopathy
based on machine learning methods. Within the framework of a rolling scheme, CNN is utilized for a group of retinal images when the images can be recognized and diagnosed quickly. The web application is built in the Flask web application platform deliberately for the purpose of providing the user a rich experience as they upload retina images and receive feedback whether there is any abnormality or not. This approach enables doctor to be present in a better position in the assessment of the general surrounding environment while patients become more conscious and responsible for their vision. Preparing the data, training the model, validating and testing it, and integrating it into a web-based platform to use the created model are all included in this. Additionally, this page explains the significance of the established model for diabetes retinopathy screening and management.