A Novel Attention-based U-net Approach for Retinal Image Segmentation Across Diverse Datasets
Year: 2025
Type: article
Abstract: Retinal vessel segmentation is essential for the early detection and diagnosis of ophthalmic and systemic diseases. This study aims to enhance the segmentation performance of the widely used U-Net architecture through novel integrations of attention mechanisms, specifically Attention U-Net (Att-UNet) and Spatial Attention U-Net (SA-UNet). To systematically evaluate these methods, comparative experiments were conducted across three benchmark retinal datasets: DRIVE, STARE, and CHASE-DB1. Models were trained for 80 epochs using a hybrid loss combining Binary Cross-Entropy and Dice loss functions, and performance was rigorously assessed using metrics such as Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and Accuracy. The experimental results indicate that attention-enhanced variants consistently outperform standard U-Net in terms of segmentation quality. Specifically, Att-UNet achieved the highest accuracy (97.37%) on DRIVE and STARE datasets, providing optimal segmentation precision, whereas SA-UNet demonstrated superior computational efficiency with the lowest training times. These findings highlight the dataset-dependent benefits and computational trade-offs associated with attention mechanisms. Overall, this study confirms that integrating novel AI techniques, such as attention mechanisms, significantly improves retinal vessel segmentation, showcasing potential for real-world engineering and clinical applications.
Authors: Omar Abdelhamed, Ahmet Saygılı
Institution: Tekirdağ Namık Kemal University
Language: English
Topic: Retinal Imaging and Analysis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4414050685