A Dual-Stage Deep Lesion Segmentation Framework with Gradient and Boundary Optimization for Diabetic Retinopathy Retinal Images

Abstract

Diabetic Retinopathy (DR) is a leading global cause of preventable blindness, and the early detection and precise segmentation of pathologies play a key role in clinical decisions in this disease. Conventional deep learning models, however, suffer from the problem of weak lesion boundaries, gradient inconsistency, and structural distortion in heterogeneous datasets. To overcome these drawbacks, we present a sophisticated systemthat combines GEONet (Gradient and Edge-Optimized Network) to achieve accurate gradient- and edge-aware segmentation, and BESNet (Boundary-Enhanced Segmentation Network) to introduce boundary refinement and structural preservation. This two-step strategy can guarantee the high-fidelity capture of subtle retinal lesions with anatomical consistency. Comparative analysis was carried out against state-of-the-art baselines. Experimental results on the EyePACS dataset demonstrate the effectiveness of the proposed framework. The integrated GEONet+BESNet architecture achieved a Dice Coefficient of 89.0%, Intersection-over-Union (IoU) of 87.0%, Boundary Accuracy of 89.0%, and a Hausdorff Distance of 5.1, outperforming all competing methods in terms of segmentation fidelity and boundary preservation. Structural consistency was further enhanced, attaining a Dice Similarity Coefficient (DSC) of 90.5% and a Structural Similarity Index Measure (SSIM) of 90.3%. From a clinical screening perspective, the proposed framework achieved a Precision of 92.8% and Specificity of 94.6%, indicating reliable lesion localization with a reduced rate of false-positive detections. These findings confirm that the synergistic integration of gradient-aware segmentation through GEONet and boundary-enhanced refinement through BESNet effectively preserves lesion morphology and improves segmentation robustness across heterogeneous retinal imaging conditions.