MultiResUNet for Precise Segmentation of OpticDisc and Blood Vessels in Diabetic Retinopathy

Significance: Diabetic Retinopathy (DR), a widespread complication of diabetes, demands advanced diagnostic tools. This research is crucial in refining DR detection, and offering potential advancements in patient care and early intervention. Objective: The main aim is to enhance automated DR diagnosis by utilizing the MultiResUNet paradigm. The main emphasis is on accurately segmenting blood vessels and optic discs to achieve higher precision and enhance diagnostic capabilities. The MultiResUNet is trained extensively using modern deep learning methods. The model is designed to accurately segment blood vessels and optic discs, enhancing its contribution to diabetic retinopathy diagnoses. Results show significant accuracy in blood vessel segmentation (87%) and optic disc segmentation (98%). This sophisticated method represents a significant advancement in automated identification of diabetic retinopathy, especially beneficial in areas with a high diabetes prevalence. Conclusion: The MultiResUNet model demonstrates high accuracy rates of 87% for blood vessels and 98% for optic discs, highlighting its effectiveness in performing precise segmentation tasks essential for automated DR diagnosis. This study highlights the importance of advanced deep-learning methods and paves the way for further enhancements in the automated detection of diabetic retinopathy.