LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation
LightVesselNet is an efficient neural network with only 75K parameters designed for retinal vessel segmentation in resource-constrained settings. It uses a compact encoder-decoder with channel and spatial attention, multi-scale feature aggregation at the bottleneck, subpixel upsampling, and edge residual connections. Experiments on five public datasets (DRIVE, STARE, CHASEDB1, FIVES, HRF) show competitive sensitivity (0.8096–0.8640) and Dice scores (0.7686–0.8649) while being more efficient than state-of-the-art models. Cross-dataset evaluation confirms generalization. It is a strong candidate for low-resource clinical deployment and mobile screening.
[2606.05354] LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation
[Submitted on 3 Jun 2026]
Title:LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation
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Abstract:Retinal blood vessel segmentation plays a vital role in the early detection of diabetic retinopathy and glaucoma. While recent deep learning models have achieved great segmentation accuracy, they typically require heavy computational resources, making real-world deployment on edge devices difficult. In this paper, we propose LightVesselNet, an efficient neural network designed for retinal vessel segmentation in a resource-constrained environment. Despite containing only 75K parameters, LightVesselNet performs competitively with much larger models. The network employs a compact encoder decoder architecture enhanced with channel and spatial attention mechanisms, a multi-scale feature aggregation module at the bottleneck, and a subpixel upsampling strategy in the decoder. A dedicated edge residual connection preserves fine vessel detail throughout decoding. Extensive experiments on five publicly available datasets: DRIVE, STARE, CHASEDB1, FIVES, and HRF, yield sensitivity scores of 0.8189, 0.8499, 0.8640, 0.8634, 0.8096, and Dice coefficients of 0.8070, 0.8072, 0.8181, 0.8649, and 0.7686, respectively. LightVesselNet shows improved efficiency (Performance vs Parameter or GFlops) compared to State-of-the-Art models. Cross-dataset evaluation confirms the model's generalisation capability. Overall, LightVesselNet is a strong candidate for deployment in low-resource clinical settings and mobile screening tools.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.05354 [cs.CV]
(or arXiv:2606.05354v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.05354
arXiv-issued DOI via DataCite (pending registration)
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From: Shadman Sobhan [view email] [v1] Wed, 3 Jun 2026 18:56:13 UTC (3,882 KB)
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