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Computer-Aided Diagnosis of Coal Workers’ Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review
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- Liton Devnath
- School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia
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- Peter Summons
- School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia
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- Suhuai Luo
- School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia
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- Dadong Wang
- Quantitative Imaging, CSIRO Data61, Marsfield, Sydney, NSW 2122, Australia
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- Kamran Shaukat
- School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia
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- Ibrahim A. Hameed
- Department of ICT and Natural Sciences, Norwegian University of Science and Technology, 7491 Trondheim, Norway
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- 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
Description
<jats:p>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.</jats:p>
Journal
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- International Journal of Environmental Research and Public Health
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International Journal of Environmental Research and Public Health 19 (11), 6439-, 2022-05-25
MDPI AG
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Details 詳細情報について
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- CRID
- 1360018295860298880
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- ISSN
- 16604601
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- Data Source
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- Crossref