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

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 qua...

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Detalles Bibliográficos
Autores principales: Devnath, Liton, Summons, Peter, Luo, Suhuai, Wang, Dadong, Shaukat, Kamran, Hameed, Ibrahim A., Aljuaid, Hanan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9180284/
https://www.ncbi.nlm.nih.gov/pubmed/35682023
http://dx.doi.org/10.3390/ijerph19116439
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author Devnath, Liton
Summons, Peter
Luo, Suhuai
Wang, Dadong
Shaukat, Kamran
Hameed, Ibrahim A.
Aljuaid, Hanan
author_facet Devnath, Liton
Summons, Peter
Luo, Suhuai
Wang, Dadong
Shaukat, Kamran
Hameed, Ibrahim A.
Aljuaid, Hanan
author_sort Devnath, Liton
collection PubMed
description 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.
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spelling pubmed-91802842022-06-10 Computer-Aided Diagnosis of Coal Workers’ Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review Devnath, Liton Summons, Peter Luo, Suhuai Wang, Dadong Shaukat, Kamran Hameed, Ibrahim A. Aljuaid, Hanan Int J Environ Res Public Health Review 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. MDPI 2022-05-25 /pmc/articles/PMC9180284/ /pubmed/35682023 http://dx.doi.org/10.3390/ijerph19116439 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Devnath, Liton
Summons, Peter
Luo, Suhuai
Wang, Dadong
Shaukat, Kamran
Hameed, Ibrahim A.
Aljuaid, Hanan
Computer-Aided Diagnosis of Coal Workers’ Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review
title Computer-Aided Diagnosis of Coal Workers’ Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review
title_full Computer-Aided Diagnosis of Coal Workers’ Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review
title_fullStr Computer-Aided Diagnosis of Coal Workers’ Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review
title_full_unstemmed Computer-Aided Diagnosis of Coal Workers’ Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review
title_short Computer-Aided Diagnosis of Coal Workers’ Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review
title_sort computer-aided diagnosis of coal workers’ pneumoconiosis in chest x-ray radiographs using machine learning: a systematic literature review
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9180284/
https://www.ncbi.nlm.nih.gov/pubmed/35682023
http://dx.doi.org/10.3390/ijerph19116439
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