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ParallelNet: multiple backbone network for detection tasks on thigh bone fracture

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Abstract

In this paper, a novel two-stage R-CNN network called ParallelNet is proposed for thigh fracture detection task. In the proposed method, multiple parallel backbone networks and a feature fusion connection structure are designed, which can extract features with different reception fields. Specifically, the first backbone network is denoted as main network, which adopted normal convolution to detect small fractures, the rest backbone networks are denoted as sub-networks which adopted dilated convolution to detect large fractures. We evaluated the proposed method on a thigh fracture dataset containing 3842 X-ray radiographs, 3484 of which is assigned as a training dataset and 358 as a testing dataset. The experiments compare the proposed method with other state-of-the-art deep learning frameworks, including Faster R-CNN, FPN, Cascade R-CNN and RetinaNet, especially DCFPN which focus on thighbone fracture detection task. Our framework achieved 87.8% AP50 and 49.3% AP75 which outperformed other state-of-the-art frameworks. Moreover, ablation experiments on the backbone numbers, connection styles, different dilation rates and the position of dilated convolution have been attempted, and the function of each hyperparameter is analyzed.

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Acknowledgements

The authors would like to thank Doctor Jinliang Wang and Fuzhou Li for their devotion in annotating and collecting the thigh fracture dataset; the radiologists in Department of Linyi People’s Hospital for their devotion on evaluating the results of the experiments; Doctor Wanquan Liu from Curtin University for his pieces of advice on revising the paper. This work is under the support of the National Natural Science Foundation of China under Grants 62073237.

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China

    Mengxuan Wang, Guoshan Zhang, Bin Guan, Xinbo Wang & Yueming Zhang

  2. Department of Radiology, Linyi People’s Hospital, Linyi, 276000, China

    Jinkun Yao

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  1. Mengxuan Wang
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  2. Jinkun Yao
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  3. Guoshan Zhang
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  4. Bin Guan
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  5. Xinbo Wang
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  6. Yueming Zhang
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Correspondence to Guoshan Zhang.

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Communicated by B. Prabhakaran.

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Wang, M., Yao, J., Zhang, G. et al. ParallelNet: multiple backbone network for detection tasks on thigh bone fracture. Multimedia Systems 27, 1091–1100 (2021). https://doi.org/10.1007/s00530-021-00783-9

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