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Balanced Convolutional Neural Networks for Pneumoconiosis Detection

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  • Chaofan Hao

    (Department of Automation, Tsinghua University, Beijing 100084, China)

  • Nan Jin

    (Chongqing Center for Disease Control and Prevention, Department of Occupational Health and Radiation Health, Chongqing 400042, China)

  • Cuijuan Qiu

    (Chongqing Center for Disease Control and Prevention, Department of Occupational Health and Radiation Health, Chongqing 400042, China)

  • Kun Ba

    (Department of Automation, Tsinghua University, Beijing 100084, China)

  • Xiaoxi Wang

    (Chongqing Center for Disease Control and Prevention, Department of Occupational Health and Radiation Health, Chongqing 400042, China)

  • Huadong Zhang

    (Chongqing Center for Disease Control and Prevention, Department of Occupational Health and Radiation Health, Chongqing 400042, China)

  • Qi Zhao

    (Chongqing Center for Disease Control and Prevention, Department of Occupational Health and Radiation Health, Chongqing 400042, China)

  • Biqing Huang

    (Department of Automation, Tsinghua University, Beijing 100084, China)

Abstract

Pneumoconiosis remains one of the most common and harmful occupational diseases in China, leading to huge economic losses to society with its high prevalence and costly treatment. Diagnosis of pneumoconiosis still strongly depends on the experience of radiologists, which affects rapid detection on large populations. Recent research focuses on computer-aided detection based on machine learning. These have achieved high accuracy, among which artificial neural network (ANN) shows excellent performance. However, due to imbalanced samples and lack of interpretability, wide utilization in clinical practice meets difficulty. To address these problems, we first establish a pneumoconiosis radiograph dataset, including both positive and negative samples. Second, deep convolutional diagnosis approaches are compared in pneumoconiosis detection, and a balanced training is adopted to promote recall. Comprehensive experiments conducted on this dataset demonstrate high accuracy (88.6%). Third, we explain diagnosis results by visualizing suspected opacities on pneumoconiosis radiographs, which could provide solid diagnostic reference for surgeons.

Suggested Citation

  • Chaofan Hao & Nan Jin & Cuijuan Qiu & Kun Ba & Xiaoxi Wang & Huadong Zhang & Qi Zhao & Biqing Huang, 2021. "Balanced Convolutional Neural Networks for Pneumoconiosis Detection," IJERPH, MDPI, vol. 18(17), pages 1-14, August.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:17:p:9091-:d:624305
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    References listed on IDEAS

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    1. Reuven Rubinstein, 1999. "The Cross-Entropy Method for Combinatorial and Continuous Optimization," Methodology and Computing in Applied Probability, Springer, vol. 1(2), pages 127-190, September.
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    Cited by:

    1. Xinglin Chen & Fuqiang Yang & Shuo Cheng & Shuaiqi Yuan, 2023. "Occupational Health and Safety in China: A Systematic Analysis of Research Trends and Future Perspectives," Sustainability, MDPI, vol. 15(19), pages 1-27, September.

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