A Lightweight Hybrid Network for Medical Image Segmentation With Adaptive Feature Selection

Accurate medical image segmentation with low model complexity remains difficult because lesions are often small in scale and boundary cues are easily corrupted by noise. Although recent segmentation methods have achieved strong performance, many of them rely on increasingly complex architectures with high computational costs, limiting their applicability in resource‐constrained settings. To alleviate this issue, we propose PLNet, a lightweight hybrid segmentation framework for medical image analysis. The network improves representation learning by integrating fine‐grained local‐structure perception with long‐range contextual modelling, thereby enhancing the representation of complex medical images. In addition, a feature selection mechanism is employed to emphasise informative feature responses and suppress redundant activations while preserving discriminative representations. Extensive experiments on multiple datasets demonstrate that PLNet achieves competitive IoU and Dice scores with relatively low model complexity. Overall, these results highlight the potential of PLNet as an effective lightweight framework that balances segmentation performance and computational complexity. The source code is available at https://github.com/lzw514601882/PLNet .