Automatic Severity Classification of Diabetic Retinopathy Using Random Forest

Diabetic Retinopathy (DR) is a severe vision-threatening condition caused by high blood glucoselevels. Traditional screening by ophthalmologists can be error-prone and time-consuming, leading toincreased interest in automated diagnostic methods. This paper introduces a deep learning approach thatuses DenseNet169 and a Random Forest model to detect DR severity from a single-Color Fundusphotograph, achieving high accuracy, sensitivity, and specificity. Trained on the Kaggle Asia Pacific TeleOphthalmology Society dataset, the model outperforms current methods and offers an efficient solution forautonomous DR diagnosis.