LiteFracNet: An Efficient Feature Interaction Network for Fracture Detection in Medical Images

Automated fracture detection remains challenging because fracture regions often exhibit low contrast, blurred boundaries, large scale variations, and substantial morphological diversity. Although deep learning-based detectors show promising performance, they often suffer from limited category coverage and insufficient multi-scale feature representation. To address these issues, we propose LiteFracNet, a lightweight framework for accurate and efficient fracture detection. C3-CFormer enhances feature representation via residual aggregation, gated dynamic modeling, and long-range dependency extraction. C2Mona improves fine-grained fracture perception using multi-scale convolution and feature separation–reconstruction. The OmniKernel Fusion Pyramid Network (OFPN) promotes cross-level feature interaction and multi-scale information propagation, improving detection of subtle fractures. The Fusion-Enhanced Detection Head (FED-Head) employs channel alignment and shared convolutions to unify multi-scale features, reducing redundancy in conventional multi-branch heads. On the HBFMID, LiteFracNet achieves a mAP50 of 93.53% with 2.41 M parameters, 8.1 GFLOPs, a 4.9 MB model size, and 87.1 FPS inference speed. It also achieves a mAP50 of 61.43% on the pediatric GRAZPEDWRI-DX dataset, demonstrating competitive cross-dataset adaptability. These results indicate that LiteFracNet has the potential to efficiently assist computers in detecting fractures.