# LFRA-Net: A Lightweight Focal and Region-Aware Attention Network for Retinal Vessel Segmentatio

arXiv: https://arxiv.org/abs/2509.11811 · HTML: https://mlsanity.com/paper/2509.11811/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 67 references that are in the map.
- First posted on arXiv 15 Sep 2025.

## Builds on (most cited first, 22 of 22)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation](https://mlsanity.com/paper/1611.06612/) (2016)
- [Learning Deconvolution Network for Semantic Segmentation](https://mlsanity.com/paper/1505.04366/) (2015)
- [MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation](https://mlsanity.com/paper/1902.04049/) (2019)
- [Focal Modulation Networks](https://mlsanity.com/paper/2203.11926/) (2022)
- [Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions](https://mlsanity.com/paper/1909.00166/) (2019)
- [IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks](https://mlsanity.com/paper/1912.05763/) (2019)
- [Retinal Vessel Segmentation via a Multi-resolution Contextual Network and Adversarial Learning](https://mlsanity.com/paper/2304.12856/) (2023)
- [MKIS-Net: A Light-Weight Multi-Kernel Network for Medical Image Segmentation](https://mlsanity.com/paper/2210.08168/) (2022)
- [RC-Net: A Convolutional Neural Network for Retinal Vessel Segmentation](https://mlsanity.com/paper/2112.11078/) (2021)
- [Feature Enhancer Segmentation Network (FES-Net) for Vessel Segmentation](https://mlsanity.com/paper/2309.03535/) (2023)
- [ESDMR-Net: A Lightweight Network With Expand-Squeeze and Dual Multiscale Residual Connections for Medical Image Segmentation](https://mlsanity.com/paper/2312.10585/) (2023)
- [Orientation and Context Entangled Network for Retinal Vessel Segmentation](https://mlsanity.com/paper/2207.11396/) (2022)
- [TBConvL-Net: A Hybrid Deep Learning Architecture for Robust Medical Image Segmentation](https://mlsanity.com/paper/2409.03367/) (2024)
- [LMBF-Net: A Lightweight Multipath Bidirectional Focal Attention Network for Multifeatures Segmentation](https://mlsanity.com/paper/2407.02871/) (2024)
- [LMBiS-Net: A Lightweight Multipath Bidirectional Skip Connection based CNN for Retinal Blood Vessel Segmentation](https://mlsanity.com/paper/2309.04968/) (2023)
- [IKD+: Reliable Low Complexity Deep Models For Retinopathy Classification](https://mlsanity.com/paper/2303.02310/) (2023)
- [The Role of AI in Early Detection of Life-Threatening Diseases: A Retinal Imaging Perspective](https://mlsanity.com/paper/2505.20810/) (2025)

## Built on by (most cited first, 3 of 3)

- [Focal Modulation and Bidirectional Feature Fusion Network for Medical Image Segmentation](https://mlsanity.com/paper/2510.20933/) (2025)
- [HMS-VesselNet: Hierarchical Multi-Scale Attention Network with Topology-Preserving Loss for Retinal Vessel Segmentation](https://mlsanity.com/paper/2603.21891/) (2026)
- [LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation](https://mlsanity.com/paper/2606.05354/) (2026)

## Abstract (arXiv)

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 CHASEDB. 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 CHASEDB 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.