Laser & Optoelectronics Progress, Volume. 58, Issue 12, 1210020(2021)

Research on Automatic Classification of Distal Radius Fractures Based on Deep Learning

Feng Yang1, Rikun Cong1, Weiguo Wang2, and Bo Ding1、*
Author Affiliations
  • 1Network Information Center of Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250355, China
  • 2The First Clinical College of Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250355, China
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    In order to solve the problem that there are many and irregular bone fragments in the focal area of the distal radius fracture, which causes the doctor’s missed diagnosis and high rate of misdiagnosis, this paper uses the clinical cases of distal radius fracture collected by the research group to propose a supervised automatic distal radius fracture deep learning model. The experiment also introduces the concept of migration learning, which improves the training efficiency of the diagnostic model. Finally, the experiment uses a cross-validation method to evaluate the model. The results show that the classification results of the proposed diagnostic model are better than traditional machine learning and classic deep learning classification models. The classification accuracy rate reaches 84.2%, which is 4% higher than the classic deep learning model. The network structure is simple, the calculation speed is fast, with certain robustness and strong generalization ability.

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    Feng Yang, Rikun Cong, Weiguo Wang, Bo Ding. Research on Automatic Classification of Distal Radius Fractures Based on Deep Learning[J]. Laser & Optoelectronics Progress, 2021, 58(12): 1210020

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    Paper Information

    Category: Image Processing

    Received: Aug. 11, 2020

    Accepted: Oct. 29, 2020

    Published Online: Jun. 21, 2021

    The Author Email: Ding Bo (dingbo@126.com)

    DOI:10.3788/LOP202158.1210020

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