Diabetic Retinopathy (DR) and Hypertensive Retinopathy (HR) are major retinal diseases contributing to global visual impairment, where early screening and progression monitoring critically depend on accurate retinal Artery/Vein segmentation and diameter estimation. To address limitations in current methods regarding vessel segmentation and geometric quantification, we propose a framework integrating both segmentation and measurement. The architecture includes three novel deep learning modules: the Hierarchical Feature Extraction and Integration Module (HFEIM) for capturing multi-scale vessel structures, the Adaptive Attention Pooling Module (AAPM) for emphasizing critical vascular regions, and the Multi-Scale Attention Residual Enhancement Module (MSAREM) to enhance detection of fine vessels. Following precise arteriovenous segmentation, we apply skeletonization to extract vessel centerlines, and reconstruct orthogonal elliptical cross-sections along the vessels. The final diameter is derived using the area-equivalent principle, ensuring physiological accuracy. Our approach demonstrates excellent segmentation accuracy and robustness across various arteriovenous segmentation datasets, offering a powerful solution to current challenges in retinal vessel segmentation and retinal diseases detection, these results demonstrate the effectiveness of the proposed model.