Strategies for Detecting Diabetic Retinopathy with Deep Learning and Image Processing

Abstract

Diabetic retinopathy is one of the most severe complications of diabetes, potentially leading to complete blindness if left untreated. Early detection is essential for effective treatment, but it presents significant challenges. Diagnosing the stage of diabetic retinopathy is particularly difficult and requires skilled interpretation of fundus images. Simplifying this detection process could greatly benefit millions. Diabetic retinopathy primarily affect working individuals with diabetes. It often involves extensive time spent analyzing  fundus images after a patient’s visit to the ophthalmologist. Our project aims to streamline this process, allowing doctors to care for more patients by speeding up result analysis and minimizing the risk of misdiagnosis, thus supporting ophthalmologists in their work. Diabetic retinopathy predominantly affects working-age individuals with diabetes. Diagnosing this condition typically requires significant time spent processing fundus images after each patient visit to the ophthalmologist. Our project aims to streamline this process, enabling doctors to see more patients due to faster result processing. Additionally, it seeks to assist ophthalmologists in preventing misdiagnoses.