LFA-Net: A Lightweight Network with LiteFusion Attention for Retinal Vessel Segmentation

• URL: http://arxiv.org/abs/2509.21738v1
• Date: Fri, 26 Sep 2025 01:07:05 GMT
• Title: LFA-Net: A Lightweight Network with LiteFusion Attention for Retinal Vessel Segmentation
• Abstract summary: 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.
• Score: 1.8242874713398711
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Lightweight retinal vessel segmentation is important for the early diagnosis of vision-threatening and systemic diseases, especially in a real-world clinical environment with limited computational resources. Although segmentation methods based on deep learning are improving, existing models are still facing challenges of small vessel segmentation and high computational costs. To address these challenges, we proposed a new vascular segmentation network, LFA-Net, which incorporates a newly designed attention module, LiteFusion-Attention. This attention module incorporates residual learning connections, Vision Mamba-inspired dynamics, and modulation-based attention, enabling the model to capture local and global context efficiently and in a lightweight manner. 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. We validated our proposed model on DRIVE, STARE, and CHASE_DB with outstanding performance in terms of dice scores of 83.28, 87.44, and 84.50% and Jaccard indices of 72.85, 79.31, and 74.70%, respectively. The code of LFA-Net is available online https://github.com/Mehwish4593/LFA-Net.

Related papers

• LCMamNet: A Lightweight Cross-scale Mamba Network for Infrared Small Target Detection [53.811713750107934]
  We propose LCMamNet, a lightweight cross-scale Mamba network for infrared small target detection.<n>A compact hierarchical encoder with cross-shaped directional bottleneck residual blocks strengthens direction-sensitive target structures.<n>Experiments on IRSTD-1k, NUAA-SIRST, and NUDT-SIRST show that the proposed network achieves mIoU scores of 71.25%, 79.60%, and 95.58%, respectively.
  arXiv  Detail & Related papers  (2026-07-27T09:06:14Z)
• 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)
• LightAVSeg: Lightweight Audio-Visual Segmentation [67.18006751024687]
  LightAVSeg replaces heavy attention with a decoupled design for semantic filtering and spatial grounding.<n>Experiments demonstrate it achieves a new state-of-the-art among lightweight methods.<n>It reaches 50.4 mIoU on the MS3 benchmark and enables efficient inference on a mobile processor.
  arXiv  Detail & Related papers  (2026-05-09T08:47:54Z)
• MambaLiteUNet: Cross-Gated Adaptive Feature Fusion for Robust Skin Lesion Segmentation [15.93793665080762]
  MambaLiteUNet is a compact yet robust segmentation framework that integrates Mamba state space modeling into a U-Net architecture.<n>MambaLiteUNet achieves a strong balance between accuracy and efficiency, making it a competitive and practical solution for dermatological image segmentation.
  arXiv  Detail & Related papers  (2026-04-22T07:33:14Z)
• LFRA-Net: A Lightweight Focal and Region-Aware Attention Network for Retinal Vessel Segmentatio [4.5211402678313135]
  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.
  arXiv  Detail & Related papers  (2025-09-15T11:47:51Z)
• A Lightweight Deep Learning Model for Automatic Modulation Classification using Dual Path Deep Residual Shrinkage Network [0.0]
  Automatic Modulation Classification (AMC) plays a key role in enhancing spectrum efficiency.<n>There is a pressing need for lightweight AMC models that balance low complexity with high classification accuracy.<n>This paper proposes a low-complexity, lightweight deep learning (DL) AMC model optimized for resource-constrained edge devices.
  arXiv  Detail & Related papers  (2025-07-07T00:37:54Z)
• 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)
• LSNet: See Large, Focus Small [67.05569159984691]
  We introduce LS (textbfLarge-textbfSmall) convolution, which combines large- kernel perception and small- kernel aggregation.<n>LSNet achieves superior performance and efficiency over existing lightweight networks in various vision tasks.
  arXiv  Detail & Related papers  (2025-03-29T16:00:54Z)
• KaLDeX: Kalman Filter based Linear Deformable Cross Attention for Retina Vessel Segmentation [46.57880203321858]
  We propose a novel network (KaLDeX) for vascular segmentation leveraging a Kalman filter based linear deformable cross attention (LDCA) module. Our approach is based on two key components: Kalman filter (KF) based linear deformable convolution (LD) and cross-attention (CA) modules. The proposed method is evaluated on retinal fundus image datasets (DRIVE, CHASE_BD1, and STARE) as well as the 3mm and 6mm of the OCTA-500 dataset.
  arXiv  Detail & Related papers  (2024-10-28T16:00:42Z)
• MobiLlama: Towards Accurate and Lightweight Fully Transparent GPT [87.4910758026772]
  "Bigger the better" has been the predominant trend in recent Large Language Models (LLMs) development. This paper explores the "less is more" paradigm by addressing the challenge of designing accurate yet efficient Small Language Models (SLMs) for resource constrained devices.
  arXiv  Detail & Related papers  (2024-02-26T18:59:03Z)
• UCM-Net: A Lightweight and Efficient Solution for Skin Lesion Segmentation using MLP and CNN [4.7894373545842805]
  UCM-Net is a novel skin lesion segmentation model combining Multi-Layer Perceptrons (MLP) and Convolutional Neural Networks (CNN) It demonstrates robust performance with fewer than 50KB parameters and requires less than 0.05 Giga Operations Per Second ( GLOPs) It is a potential benchmark for efficiency in skin lesion segmentation, suitable for deployment in resource-constrained settings.
  arXiv  Detail & Related papers  (2023-10-14T00:32:11Z)
• FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification [65.7405397206767]
  We propose Federated Deep-Broad Learning (FedDBL) to achieve superior classification performance with limited training samples and only one-round communication. FedDBL greatly outperforms the competitors with only one-round communication and limited training samples, while it even achieves comparable performance with the ones under multiple-round communications. Since no data or deep model sharing across different clients, the privacy issue is well-solved and the model security is guaranteed with no model inversion attack risk.
  arXiv  Detail & Related papers  (2023-02-24T14:27:41Z)
• Rethinking Mobile Block for Efficient Attention-based Models [60.0312591342016]
  This paper focuses on developing modern, efficient, lightweight models for dense predictions while trading off parameters, FLOPs, and performance. Inverted Residual Block (IRB) serves as the infrastructure for lightweight CNNs, but no counterpart has been recognized by attention-based studies. We extend CNN-based IRB to attention-based models and abstracting a one-residual Meta Mobile Block (MMB) for lightweight model design.
  arXiv  Detail & Related papers  (2023-01-03T15:11:41Z)
• Lightweight and Progressively-Scalable Networks for Semantic Segmentation [100.63114424262234]
  Multi-scale learning frameworks have been regarded as a capable class of models to boost semantic segmentation. In this paper, we thoroughly analyze the design of convolutional blocks and the ways of interactions across multiple scales. We devise Lightweight and Progressively-Scalable Networks (LPS-Net) that novelly expands the network complexity in a greedy manner.
  arXiv  Detail & Related papers  (2022-07-27T16:00:28Z)

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.