{"ID":2870870,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.11811","arxiv_id":"2509.11811","title":"LFRA-Net: A Lightweight Focal and Region-Aware Attention Network for Retinal Vessel Segmentatio","abstract":"Retinal vessel segmentation is critical for the early diagnosis of vision-threatening and systemic diseases, especially in real-world clinical settings with limited computational resources. Although significant improvements have been made in deep learning-based segmentation methods, current models still face challenges in extracting tiny vessels and suffer from high computational costs. In this study, we present LFRA-Net by incorporating focal modulation attention at the encoder-decoder bottleneck and region-aware attention in the selective skip connections. LFRA-Net is a lightweight network optimized for precise and effective retinal vascular segmentation. It enhances feature representation and regional focus by efficiently capturing local and global dependencies. LFRA-Net outperformed many state-of-the-art models while maintaining lightweight characteristics with only 0.17 million parameters, 0.66 MB memory size, and 10.50 GFLOPs. We validated it on three publicly available datasets: DRIVE, STARE, and CHASE\\_DB. It performed better in terms of Dice score (84.28\\%, 88.44\\%, and 85.50\\%) and Jaccard index (72.86\\%, 79.31\\%, and 74.70\\%) on the DRIVE, STARE, and CHASE\\_DB datasets, respectively. LFRA-Net provides an ideal ratio between segmentation accuracy and computational cost compared to existing deep learning methods, which makes it suitable for real-time clinical applications in areas with limited resources. The code can be found at https://github.com/Mehwish4593/LFRA-Net.","short_abstract":"Retinal vessel segmentation is critical for the early diagnosis of vision-threatening and systemic diseases, especially in real-world clinical settings with limited computational resources. Although significant improvements have been made in deep learning-based segmentation methods, current models still face challenges...","url_abs":"https://arxiv.org/abs/2509.11811","url_pdf":"https://arxiv.org/pdf/2509.11811v1","authors":"[\"Mehwish Mehmood\",\"Shahzaib Iqbal\",\"Tariq Mahmood Khan\",\"Ivor Spence\",\"Muhammad Fahim\"]","published":"2025-09-15T11:47:51Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":609804,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2870870,"paper_url":"https://arxiv.org/abs/2509.11811","paper_title":"LFRA-Net: A Lightweight Focal and Region-Aware Attention Network for Retinal Vessel Segmentatio","repo_url":"https://github.com/Mehwish4593/LFRA-Net","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}