Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Applied Sciences

DOI: 10.3390/app16157484

Authors: Xi Chen, Guohui Wang, Yanting Lu

Institution: Xi'an Technological University

Language: English

Topic: Medical Imaging and Analysis

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funders: Education Department of Shaanxi Province, Xi'an Technological University