DIAGNOSIS AND CLASSIFICATION OF DIABETIC RETINOPATHY WITH YOLOv8-BASED DEEP LEARNING MODEL

Diabetic retinopathy is a complication of diabetes that affects the eyes. High blood sugar levels damage the vessels of the retina, damaging the light-sensing cells in the eye, and can cause vision loss and, in severe cases, blindness. Deep learning models are powerful tools that can process and learn large data sets, and if used in the diagnosis of diabetes and diabetic retinopathy, they can benefit the early diagnosis of the disease. Deep learning enables early and high-accuracy detection of diabetic retinopathy symptoms with high sensitivity and specificity, as well as minimizing errors made by experts. In this study, we aimed to detect and classify diabetic retinopathy using the YOLOv8 (You Only Look Once) model, one of the CNN (convolutional neural network) architectures. The experimental studies were conducted with two different CPUs and two different GPUs. As a result of the experimental studies, the highest accuracy value was obtained as 84.91% with GPU1, and the average accuracy across the four different methods (CPU1, CPU2, GPU1, GPU2) was 83.82%.