Diabetic Retinopathy Prediction using YOLOv8

Abstract: Diabetic retinopathy (DR), a leading cause of blindness in diabetic patients, is traditionally diagnosed through the manual examination of retinal images. This process is resource-intensive and susceptible to human error. In response, this research introduces an innovative approach for the early detection of DR using YOLOv8, a state-of-the-art deep learning model renowned for its real-time object detection capabilities. By automating the detection process, our system focuses on identifying critical DR features, such as microaneurysms, hemorrhages, and exudates, which are indicators of disease progression. The methodology begins with thorough preprocessing of retinal images, including resizing, normalization, and contrast enhancement, to ensure high-quality inputs for model training. YOLOv8 is then trained to accurately locate and classify these DR features, demonstrating high performance across various stages of the disease. Key evaluation metrics, such as mean Average Precision (mAP), precision, recall, and F1-score, validate the model’s efficiency and accuracy, making it suitable for deployment in real-world clinical settings. Beyond its clinical applications, this study explores the potential integration of YOLOv8 into large-scale screening programs, particularly in underserved regions with limited access to specialized eye care. Future directions include enhancing the model’s generalizability, exploring ensemble learning techniques for improved accuracy, and investigating its potential for detecting other retinal diseases. This research contributes to the broader field of AI-driven healthcare, aiming to improve early diagnosis, reduce diagnostic costs, and support preventive care in the management of diabetic retinopathy. [2]