Accurate delineation of colorectal polyps in colonoscopy images is a key prerequisite for a computer-aided diagnosis （CAD） in colorectal cancer （CRC） screening，as adenomatous polyps are common precursors of CRC，and their timely detection and removal can substantially reduce incidence and mortality. However，in routine endoscopy，automated polyp segmentation remains challenging for three main reasons. First，polyp boundaries are frequently indistinct： subtle intensity transitions，mucus coverage，and folds of surrounding mucosa can obscure lesion contours，making boundary localization error-prone. Second，polyp appearance markedly varies across size，shape，texture，and attachment morphology. This diversity complicates stable feature representation and can trigger over-segmentation （including mucosal folds） or under-segmentation （missing flat or small lesions）. Third，colonoscopy imaging often exhibits non-uniform illumination，specular highlights，and lens-related artifacts. These factors distort local contrast and degrade model robustness. Meanwhile，practical deployment introduces additional constraints： endoscopy systems typically process high-resolution streams at high frame rates and must respond with low latency on resource-limited hardware. Accordingly，the objective of this study is to develop a lightweight yet accurate polyp segmentation model that 1） balances global semantic understanding with fine-grained boundary detail，2） achieves efficiency suitable for routine clinical workflows under deployment constraints，and 3） generalizes robustly across heterogeneous datasets collected with different devices and acquisition conditions.

We propose ViBound-Net，a lightweight hybrid-attention segmentation network designed to jointly emphasize semantic context and boundary fidelity while maintaining a compact computational footprint. The network adopts EfficientNetV2S as a lightweight encoder to provide strong feature extraction with favorable parameter-compute efficiency. We introduce a local-global contextual attention fusion （LGCAF） module that integrates fine structural cues （effectively captured by local receptive fields） with global contextual dependencies （captured via attention） to address the local-global trade-off that commonly limits purely convolutional or purely attention-based approaches. This fusion aims to improve semantic consistency in challenging backgrounds while preserving lesion-specific structural patterns that are essential for small or camouflaged polyps. We further design a boundary-guided scale-aware feature enhancement （BSFE） module to explicitly strengthen boundary modeling. BSFE leverages multi-scale representations and boundary-aware guidance to refine edge-related features，targeting failure modes，such as contour breaks，boundary blurring，and partial omission around low-contrast margins. In decoding，we utilized a multi-scale residual attention fusion block to aggregate cross-scale information while stabilizing optimization through residual pathways. This design encourages coherent reconstruction of polyp regions across scales and reduces sensitivity to size variability. In addition，compact channel-spatial attention is incorporated to promote feature consistency and adaptively emphasize salient lesion cues without introducing heavy computation. Deep supervision is used to guide intermediate predictions and improve convergence stability，particularly for boundary-sensitive learning. Model training adopts a compound Dice + focal loss to mitigate foreground-background imbalance and enhance learning stability under diverse lesion sizes and appearance variations. Images are resized to 352 × 352，normalized，and augmented using random scaling，flipping，and rotation to improve robustness to viewpoint and scale changes commonly observed in endoscopy. Optimization follows a unified protocol using AdamW （learning rate 1 × 10⁻⁴，batch size 4） to ensure a fair comparison across methods. Experiments are conducted on three widely used public colonoscopy benchmarks——Kvasir-SEG，CVC-ClinicDB，and CVC-300——which collectively cover diverse resolutions，polyp sizes and shapes，and background complexity，for evaluation. We report segmentation accuracy using Dice similarity coefficient （Dice） and mean intersection over union （mIoU）. Moreover，we report the mean absolute error （MAE） to effectively reflect boundary sensitivity and contour adherence. Given that deployability is an explicit goal，we assess the model efficiency through parameter count，FLOPs，and per-image inference time under the same experimental protocol and input scale. Finally，we provide qualitative visual comparisons to analyze contour adherence，over-/under-segmentation behaviors，and robustness under low contrast，specular highlights，and lesions that resemble surrounding mucosa.

Under the unified evaluation protocol，ViBound-Net achieves strong performance across all three benchmarks. Specifically，this model attains mDice/mIoU of 0.908 1/0.852 7 on Kvasir-SEG，0.936 8/0.887 8 on CVC-ClinicDB，and 0.871 0/0.798 9 on CVC-300. In boundary-sensitive evaluation on the ETIS-LaribPolypDB dataset，ViBound-Net reports an MAE of 0.011 7，indicating improved contour sharpness and faithful delineation. These results suggest that the proposed local-global fusion and boundary-guided enhancement effectively reduce common segmentation artifacts，such as contour leakage into mucosal folds，fragmented edges around flat polyps，and missing regions under low contrast. ViBound-Net delivers competitive or superior accuracy on most metrics while maintaining a compact design compared with strong convolutional and transformer-based baselines under the same protocol. For example，on Kvasir-SEG，this model improves over M2SNet in mDice and mIoU by 2.6 and 3.5 percentage points，respectively，and it reduces boundary error relative to DUCK-Net，reflecting a reliable edge localization. Across datasets，qualitative comparisons show that ViBound-Net produces smooth，better-adhered contours with few over-/under-segmentation artifacts，particularly for small lesions，low-contrast polyps，and cases affected by specular highlights. From an efficiency perspective，ViBound-Net consistently demonstrates lower computational burden than many transformer-heavy counterparts （as reflected by fewer parameters and reduced FLOPs），supporting deployment-oriented constraints where latency and memory are critical. Overall，these results indicate that the proposed architecture provides a favorable accuracy-efficiency trade-off，aligning with the practical requirements of routine endoscopy systems.

This study presents ViBound-Net，a lightweight hybrid-attention network for colorectal polyp segmentation that explicitly targets the simultaneous demands of semantic reliability，boundary precision，and deployment efficiency. ViBound-Net achieves a high segmentation accuracy and a low boundary error on three public benchmarks while remaining computationally efficient by combining a compact EfficientNetV2S encoder with a LGCAF module and a boundary-guided scale-aware feature enhancement module and reinforcing multi-scale decoding via residual attention fusion and compact channel-spatial attention. These findings support the feasibility of integrating ViBound-Net into CAD pipelines for CRC screening，where stable boundary delineation can assist downstream clinical tasks，such as polyp characterization，measurement，and resection guidance. Future work will extend the method in several directions： 1） incorporating temporal cues to exploit video continuity and minimize frame-to-frame inconsistency in real endoscopy streams； 2） improving cross-device and cross-center robustness through explicit domain adaptation strategies； 3） enhancing interpretability to effectively communicate model decisions to clinicians； and 4） conducting prospective clinical validation to quantify the workflow-level effect，including time savings，detection consistency，and potential reduction of missed lesions in routine practice.