Multi-model approach for precise lesion localization and severity grading for diabetic retinopathy and age-related macular degeneration

Introduction Accurate and efficient automated diagnosis of Diabetic Retinopathy (DR) and Age-related Macular Degeneration (AMD) is crucial for addressing these leading causes of vision loss worldwide. Driven by the potential to improve early detection and patient outcomes, this study proposes a comprehensive system for diagnosing and grading these conditions. Methods Our approach combines image enhancement techniques, automated lesion localization, and disease severity classification. The study utilizes both established benchmark datasets and four newly proposed datasets to ensure robust evaluation. Results The localization model achieved exceptional performance with mAP scores of up to 98.71% for AMD on the Shiromoni_AMD dataset and 97.21% for DR on the KLC_DR dataset. Similarly, the severity classification model demonstrated high accuracy, reaching 99.42% for AMD on the Stare dataset and 98.81% for DR on the KLC_DR dataset. Comparative analysis shows that our proposed methods often surpass existing state-of-the-art approaches, demonstrating more consistent performance across diverse datasets and eye conditions. Discussion This research represents a significant advancement in automated ophthalmic diagnosis, potentially enhancing clinical practice and improving accessibility to eye care worldwide. Our findings pave the way for more accurate, efficient, and widely applicable automated screening tools for retinal diseases.