Clinically Aligned AI for Diabetic Retinopathy: Interpretable Grading Based on Lesion Segmentation

ABSTRACT Retinopathy, a prevalent retinal disorder, poses a major risk of vision loss if not detected at an early stage. Automatic lesion segmentation plays a key role in effective diagnosis and disease monitoring. In this work, we present a complete and interpretable pipeline that combines YOLOv12 for lesion segmentation, SVD‐CAM for visual explanation, and transformer‐based Gradient Boosted Neural Networks (GBNN) and Random Forest classifiers for diabetic retinopathy (DR) severity grading. YOLOv12 (You Only Look Once, version 12), known for its real‐time object detection capability, is adapted to the complex task of retinal lesion segmentation, delivering both high accuracy and speed. To enhance lesion localization, SVD‐CAM generates precise heatmaps that highlight critical pathological regions influencing the grading decision. The segmented lesions are then quantified and used as input features for the grading stage, enabling clinically aligned DR classification. Our approach not only achieves state‐of‐the‐art performance across three public datasets (IDRiD, DDR, and FGADR) but also provides lesion‐level interpretability that improves clinical trust and adoption. Extensive experiments demonstrate that the proposed framework delivers accurate segmentation, reliable grading, and meaningful visual explanations, establishing a robust solution for automated DR analysis.