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

Liton Devnath, Peter Summons, Suhuai Luo, Dadong Wang, Kamran Shaukat, Ibrahim A. Hameed and Hanan Aljuaid
Additional contact information
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
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

IJERPH, 2022, vol. 19, issue 11, 1-22

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.

Keywords: coal workers’ pneumoconiosis; computer-aided diagnostic; occupational lung disease; pneumoconiosis; black lung; texture feature analysis; machine learning; deep learning; chest X-ray radiographs; systematic literature review (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1660-4601/19/11/6439/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/11/6439/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:19:y:2022:i:11:p:6439-:d:824104

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().