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<p>Pediatric wrist fracture detection is diagnostically challenging due to subtle fracture morphologies and growth plate obscuration. To address this, we propose FracDet-v11, a specialized real-time framework based on YOLOv11s. We introduce a series of architectural enhancements to optimize feature representation and detection robustness: (1) a reconstructed backbone integrating Haar Wavelet Downsampling (HWD) and PKI-CAA to effectively preserve high-frequency details, complemented by a Dual-branch Channel Attention Module (DCAM); (2) a lightweight Slim-Neck for efficient feature fusion; and (3) a detection head incorporating Deformable Convolution v4 (DCNv4) and Focaler-CIoU loss to adapt to geometric deformations and prioritize hard samples. Benchmarking on the GRAZPEDWRI-DX dataset demonstrates that FracDet-v11 achieves a precision of 73.9% and mAP50 of 64.8%, surpassing the baseline by 3.8% and 3.1%, respectively. Furthermore, the model exhibits robust generalization on the external FracAtlas dataset with an mAP50 of 47.9%, confirming its potential as a reliable assistive tool for clinical diagnosis. The code for this work is available on GitHub at https://github.com/boboji1233/FracDet-v11.</p>

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FracDet-v11: a multi-scale attention and wavelet-enhanced network for real-time pediatric wrist fracture detection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月21日

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  • Haifeng Qiu,
  • Li Liu,
  • Jiale Hong,
  • Yong He,
  • Lin He,
  • Yadong Luo

摘要

Pediatric wrist fracture detection is diagnostically challenging due to subtle fracture morphologies and growth plate obscuration. To address this, we propose FracDet-v11, a specialized real-time framework based on YOLOv11s. We introduce a series of architectural enhancements to optimize feature representation and detection robustness: (1) a reconstructed backbone integrating Haar Wavelet Downsampling (HWD) and PKI-CAA to effectively preserve high-frequency details, complemented by a Dual-branch Channel Attention Module (DCAM); (2) a lightweight Slim-Neck for efficient feature fusion; and (3) a detection head incorporating Deformable Convolution v4 (DCNv4) and Focaler-CIoU loss to adapt to geometric deformations and prioritize hard samples. Benchmarking on the GRAZPEDWRI-DX dataset demonstrates that FracDet-v11 achieves a precision of 73.9% and mAP50 of 64.8%, surpassing the baseline by 3.8% and 3.1%, respectively. Furthermore, the model exhibits robust generalization on the external FracAtlas dataset with an mAP50 of 47.9%, confirming its potential as a reliable assistive tool for clinical diagnosis. The code for this work is available on GitHub at https://github.com/boboji1233/FracDet-v11.