Transformer-Enhanced Retinal Vessel Segmentation for Diabetic Retinopathy Detection Using Attention Mechanisms and Multi-Scale Fusion

Abstract

Eye health has become a significant concern in recent years, given the rising prevalence of visual impairment resulting from various eye disorders and related factors. Global surveys suggest that approximately 2.2 billion individuals are visually impaired, with at least 1 billion affected by treatable diseases or ailments. Early detection, treatment, and screening for fundus diseases are crucial in addressing these challenges. In this study, we propose a novel segmentation model for retinal vascular delineation aimed at diagnosing diabetic retinopathy. The model integrates CBAM (Channel-Attention and Spatial-Attention) for enhanced feature representation, JPU (Joint Pyramid Upsampling) for multi-scale feature fusion, and transformer blocks for contextual understanding. Leveraging deep-learning techniques, our proposed model outperforms existing approaches in retinal vascular segmentation, like achieving a Mean IOU of 0.8047, Recall of 0.7254, Precision of 0.8492, F1 Score of 0.7824, and Specificity of 0.9892 for CHASEDB1 dataset. Extensive evaluations on benchmark datasets demonstrate its efficacy, highlighting its potential for automated diabetic retinopathy screening.

Keywords

• retinal vessel segmentation
• transformer
• joint pyramid upsampling
• attention mechanisms
• multi-scale analysis