IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i17p9091-d624305.html
   My bibliography  Save this article

Balanced Convolutional Neural Networks for Pneumoconiosis Detection

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/17/9091/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/18/17/9091/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Catanzaro, Daniele & Pesenti, Raffaele & Ronco, Roberto, 2023. "Job scheduling under Time-of-Use energy tariffs for sustainable manufacturing: a survey," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1091-1109.
    2. Mattrand, C. & Bourinet, J.-M., 2014. "The cross-entropy method for reliability assessment of cracked structures subjected to random Markovian loads," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 171-182.
    3. R. Y. Rubinstein, 2005. "A Stochastic Minimum Cross-Entropy Method for Combinatorial Optimization and Rare-event Estimation," Methodology and Computing in Applied Probability, Springer, vol. 7(1), pages 5-50, March.
    4. K.-P. Hui & N. Bean & M. Kraetzl & Dirk Kroese, 2005. "The Cross-Entropy Method for Network Reliability Estimation," Annals of Operations Research, Springer, vol. 134(1), pages 101-118, February.
    5. András Lörincz & Zsolt Palotai & Gábor Szirtes, 2012. "Efficient Sparse Coding in Early Sensory Processing: Lessons from Signal Recovery," PLOS Computational Biology, Public Library of Science, vol. 8(3), pages 1-14, March.
    6. Laijun Zhao & Huiyong Li & Yan Sun & Rongbing Huang & Qingmi Hu & Jiajia Wang & Fei Gao, 2017. "Planning Emergency Shelters for Urban Disaster Resilience: An Integrated Location-Allocation Modeling Approach," Sustainability, MDPI, vol. 9(11), pages 1-20, November.
    7. Min Xie & Yuxin Du & Peijun Cheng & Wei Wei & Mingbo Liu, 2019. "A Cross-Entropy-Based Hybrid Membrane Computing Method for Power System Unit Commitment Problems," Energies, MDPI, vol. 12(3), pages 1-18, February.
    8. Jian Yang & Huadong Cheng, 2023. "Coupling Coordination between University Scientific & Technological Innovation and Sustainable Economic Development in China," Sustainability, MDPI, vol. 15(3), pages 1-14, January.
    9. Fahimnia, Behnam & Sarkis, Joseph & Eshragh, Ali, 2015. "A tradeoff model for green supply chain planning:A leanness-versus-greenness analysis," Omega, Elsevier, vol. 54(C), pages 173-190.
    10. Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2022. "An automated prior robustness analysis in Bayesian model comparison," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 583-602, April.
    11. Reuven Y. Rubinstein, 2006. "How Many Needles are in a Haystack, or How to Solve #P-Complete Counting Problems Fast," Methodology and Computing in Applied Probability, Springer, vol. 8(1), pages 5-51, March.
    12. Ad Ridder & Bruno Tuffin, 2012. "Probabilistic Bounded Relative Error Property for Learning Rare Event Simulation Techniques," Tinbergen Institute Discussion Papers 12-103/III, Tinbergen Institute.
    13. Jorge Oyola & Halvard Arntzen & David L. Woodruff, 2017. "The stochastic vehicle routing problem, a literature review, Part II: solution methods," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 6(4), pages 349-388, December.
    14. Satyajith Amaran & Nikolaos V. Sahinidis & Bikram Sharda & Scott J. Bury, 2016. "Simulation optimization: a review of algorithms and applications," Annals of Operations Research, Springer, vol. 240(1), pages 351-380, May.
    15. Ad Ridder, 2004. "Importance Sampling Simulations of Markovian Reliability Systems using Cross Entropy," Tinbergen Institute Discussion Papers 04-018/4, Tinbergen Institute.
    16. Sigurdur Ólafsson, 2004. "Two-Stage Nested Partitions Method for Stochastic Optimization," Methodology and Computing in Applied Probability, Springer, vol. 6(1), pages 5-27, March.
    17. Saghaei, Mahsa & Ghaderi, Hadi & Soleimani, Hamed, 2020. "Design and optimization of biomass electricity supply chain with uncertainty in material quality, availability and market demand," Energy, Elsevier, vol. 197(C).
    18. Tito Homem-de-Mello, 2007. "A Study on the Cross-Entropy Method for Rare-Event Probability Estimation," INFORMS Journal on Computing, INFORMS, vol. 19(3), pages 381-394, August.
    19. Masoud Esmaeilikia & Behnam Fahimnia & Joeseph Sarkis & Kannan Govindan & Arun Kumar & John Mo, 2016. "A tactical supply chain planning model with multiple flexibility options: an empirical evaluation," Annals of Operations Research, Springer, vol. 244(2), pages 429-454, September.
    20. Fahimnia, Behnam & Sarkis, Joseph & Choudhary, Alok & Eshragh, Ali, 2015. "Tactical supply chain planning under a carbon tax policy scheme: A case study," International Journal of Production Economics, Elsevier, vol. 164(C), pages 206-215.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:17:p:9091-:d:624305. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.