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

• URL: http://arxiv.org/abs/2509.11811v1
• Date: Mon, 15 Sep 2025 11:47:51 GMT
• Title: LFRA-Net: A Lightweight Focal and Region-Aware Attention Network for Retinal Vessel Segmentatio
• Abstract summary: LFRA-Net is a lightweight network optimized for precise and effective retinal vascular segmentation.<n>It enhances feature representation and regional focus by efficiently capturing local and global dependencies.<n>LFRA-Net provides an ideal ratio between segmentation accuracy and computational cost compared to existing deep learning methods.
• Score: 4.5211402678313135
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation [0.0]
  LightVesselNet is an efficient neural network designed for retinal vessel segmentation in a resource-constrained environment.<n>LightVesselNet shows improved efficiency (Performance vs Flops) compared to State-of-the-Art models.<n>It is a strong candidate for deployment in low-resource clinical settings and mobile screening tools.
  arXiv  Detail & Related papers  (2026-06-03T18:56:13Z)
• CLFSeg: A Fuzzy-Logic based Solution for Boundary Clarity and Uncertainty Reduction in Medical Image Segmentation [2.28126966226433]
  This paper introduces CLFSeg, an encoder-decoder based framework that aggregates the Fuzzy-Convolutional (FC) module.<n>This module enhances the segmentation performance by identifying local and global features while minimizing the uncertainty, noise, and ambiguity in boundary regions.<n>Our proposed model exhibits exceptional performance on four publicly available datasets, including CVC-ColonDB, CVC-ClinicDB, EtisLaribPolypDB, and ACDC.
  arXiv  Detail & Related papers  (2025-10-28T09:06:27Z)
• LFA-Net: A Lightweight Network with LiteFusion Attention for Retinal Vessel Segmentation [1.8242874713398711]
  We propose a new vascular segmentation network, LFA-Net, which incorporates a newly designed attention module, LiteFusion-Attention.<n> LFA-Net offers high performance with 0.11 million parameters, 0.42 MB memory size, and 4.46 GFLOPs, which make it ideal for resource-constrained environments.
  arXiv  Detail & Related papers  (2025-09-26T01:07:05Z)
• FocusNet: Transformer-enhanced Polyp Segmentation with Local and Pooling Attention [11.964732823289333]
  Colonoscopy is vital in the early diagnosis of colorectal polyps.<n>Regular screenings can effectively prevent benign polyps from progressing to CRC.<n>We propose FocusNet, a Transformer-enhanced focus attention network to improve polyp segmentation.
  arXiv  Detail & Related papers  (2025-04-18T09:59:26Z)
• Enhancing Semantic Segmentation with Adaptive Focal Loss: A Novel Approach [21.134077729826107]
  We introduce an Adaptive Focal Loss function designed to mitigate class imbalance by down-weighting the loss for easy examples. We evaluate the performance of the A-FL using ResNet50-encoded U-Net architecture on the Picai 2022 and BraTS 2018 datasets. This work highlights A-FL's potential to improve deep learning models for segmenting clinically significant regions in medical images.
  arXiv  Detail & Related papers  (2024-07-13T09:41:20Z)
• LATUP-Net: A Lightweight 3D Attention U-Net with Parallel Convolutions for Brain Tumor Segmentation [7.1789008189318455]
  LATUP-Net is a lightweight 3D ATtention U-Net with Parallel convolutions. It is specifically designed to reduce computational requirements significantly while maintaining high segmentation performance. It achieves promising segmentation performance: the average Dice scores for the whole tumor, tumor core, and enhancing tumor on the BraTS 2020 dataset are 88.41%, 83.82%, and 73.67%, and on the BraTS 2021 dataset, they are 90.29%, 89.54%, and 83.92%, respectively.
  arXiv  Detail & Related papers  (2024-04-09T00:05:45Z)
• ELGC-Net: Efficient Local-Global Context Aggregation for Remote Sensing Change Detection [65.59969454655996]
  We propose an efficient change detection framework, ELGC-Net, which leverages rich contextual information to precisely estimate change regions. Our proposed ELGC-Net sets a new state-of-the-art performance in remote sensing change detection benchmarks. We also introduce ELGC-Net-LW, a lighter variant with significantly reduced computational complexity, suitable for resource-constrained settings.
  arXiv  Detail & Related papers  (2024-03-26T17:46:25Z)
• Leveraging Frequency Domain Learning in 3D Vessel Segmentation [50.54833091336862]
  In this study, we leverage Fourier domain learning as a substitute for multi-scale convolutional kernels in 3D hierarchical segmentation models. We show that our novel network achieves remarkable dice performance (84.37% on ASACA500 and 80.32% on ImageCAS) in tubular vessel segmentation tasks.
  arXiv  Detail & Related papers  (2024-01-11T19:07:58Z)
• Center Focusing Network for Real-Time LiDAR Panoptic Segmentation [58.1194137706868]
  A novel center focusing network (CFNet) is introduced to achieve accurate and real-time LiDAR panoptic segmentation. CFFE is proposed to explicitly understand the relationships between the original LiDAR points and virtual instance centers. Our CFNet outperforms all existing methods by a large margin and is 1.6 times faster than the most efficient method.
  arXiv  Detail & Related papers  (2023-11-16T01:52:11Z)
• Multi-Scale Context-Guided Lumbar Spine Disease Identification with Coarse-to-fine Localization and Classification [22.62393344071125]
  This work introduces a multi-scale context-guided network with coarse-to-fine localization and classification, named CCF-Net, for lumbar spine disease identification. The experimental results show that the coarse-to-fine design presents the potential to achieve high performance with fewer parameters and data requirements.
  arXiv  Detail & Related papers  (2022-03-16T05:51:16Z)
• Lightweight Salient Object Detection in Optical Remote Sensing Images via Feature Correlation [93.80710126516405]
  We propose a novel lightweight ORSI-SOD solution, named CorrNet, to address these issues. By reducing the parameters and computations of each component, CorrNet ends up having only 4.09M parameters and running with 21.09G FLOPs. Experimental results on two public datasets demonstrate that our lightweight CorrNet achieves competitive or even better performance compared with 26 state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-01-20T08:28:01Z)
• Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning [61.488646649045215]
  Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices)
  arXiv  Detail & Related papers  (2021-11-28T19:03:39Z)
• Efficient Regional Memory Network for Video Object Segmentation [56.587541750729045]
  We propose a novel local-to-local matching solution for semi-supervised VOS, namely Regional Memory Network (RMNet) The proposed RMNet effectively alleviates the ambiguity of similar objects in both memory and query frames. Experimental results indicate that the proposed RMNet performs favorably against state-of-the-art methods on the DAVIS and YouTube-VOS datasets.
  arXiv  Detail & Related papers  (2021-03-24T02:08:46Z)
• LRNNet: A Light-Weighted Network with Efficient Reduced Non-Local Operation for Real-Time Semantic Segmentation [15.010572800399057]
  This paper introduces a light-weighted network with an efficient reduced non-local module (LRNNet) for efficient and realtime semantic segmentation. Experiments demonstrate our superior trade-off among light-weight, speed, computation and accuracy.
  arXiv  Detail & Related papers  (2020-06-04T08:55:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.