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Computer-Aided Diagnosis of Coal Workers’ Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review

Author

Listed:
  • Liton Devnath

    (School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia)

  • Peter Summons

    (School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia)

  • Suhuai Luo

    (School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia)

  • Dadong Wang

    (Quantitative Imaging, CSIRO Data61, Marsfield, Sydney, NSW 2122, Australia)

  • Kamran Shaukat

    (School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia
    Department of Data Science, University of the Punjab, Lahore 54890, Pakistan)

  • Ibrahim A. Hameed

    (Department of ICT and Natural Sciences, Norwegian University of Science and Technology, 7491 Trondheim, Norway)

  • Hanan Aljuaid

    (Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU), P.O. Box 84428, Riyadh 11671, Saudi Arabia)

Abstract

Computer-aided diagnostic (CAD) systems can assist radiologists in detecting coal workers’ pneumoconiosis (CWP) in their chest X-rays. Early diagnosis of the CWP can significantly improve workers’ survival rate. The development of the CAD systems will reduce risk in the workplace and improve the quality of chest screening for CWP diseases. This systematic literature review (SLR) amis to categorise and summarise the feature extraction and detection approaches of computer-based analysis in CWP using chest X-ray radiographs (CXR). We conducted the SLR method through 11 databases that focus on science, engineering, medicine, health, and clinical studies. The proposed SLR identified and compared 40 articles from the last 5 decades, covering three main categories of computer-based CWP detection: classical handcrafted features-based image analysis, traditional machine learning, and deep learning-based methods. Limitations of this review and future improvement of the review are also discussed.

Suggested Citation

  • Liton Devnath & Peter Summons & Suhuai Luo & Dadong Wang & Kamran Shaukat & Ibrahim A. Hameed & Hanan Aljuaid, 2022. "Computer-Aided Diagnosis of Coal Workers’ Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review," IJERPH, MDPI, vol. 19(11), pages 1-22, May.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:11:p:6439-:d:824104
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    References listed on IDEAS

    as
    1. Lei Han & Ruhui Han & Xiaoming Ji & Ting Wang & Jingjin Yang & Jiali Yuan & Qiuyun Wu & Baoli Zhu & Hengdong Zhang & Bangmei Ding & Chunhui Ni, 2015. "Prevalence Characteristics of Coal Workers’ Pneumoconiosis (CWP) in a State-Owned Mine in Eastern China," IJERPH, MDPI, vol. 12(7), pages 1-12, July.
    2. Lei Han & Qianqian Gao & Jingjin Yang & Qiuyun Wu & Baoli Zhu & Hengdong Zhang & Bangmei Ding & Chunhui Ni, 2017. "Survival Analysis of Coal Workers’ Pneumoconiosis (CWP) Patients in a State-Owned Mine in the East of China from 1963 to 2014," IJERPH, MDPI, vol. 14(5), pages 1-10, May.
    3. Hsiao-Yu Yang, 2019. "Prediction of pneumoconiosis by serum and urinary biomarkers in workers exposed to asbestos-contaminated minerals," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-12, April.
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    Cited by:

    1. Liton Devnath & Zongwen Fan & Suhuai Luo & Peter Summons & Dadong Wang, 2022. "Detection and Visualisation of Pneumoconiosis Using an Ensemble of Multi-Dimensional Deep Features Learned from Chest X-rays," IJERPH, MDPI, vol. 19(18), pages 1-21, September.
    2. Bocheng Li & Yunqiu Zhang & Xusheng Wu, 2022. "DLKN-MLC: A Disease Prediction Model via Multi-Label Learning," IJERPH, MDPI, vol. 19(15), pages 1-15, August.

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