{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T03:44:54Z","timestamp":1790480694014,"version":"4.1.0"},"reference-count":112,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T00:00:00Z","timestamp":1653436800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Princess Nourah bint Abdulrahman University","award":["PNURSP2022R54"],"award-info":[{"award-number":["PNURSP2022R54"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>Computer-aided diagnostic (CAD) systems can assist radiologists in detecting coal workers\u2019 pneumoconiosis (CWP) in their chest X-rays. Early diagnosis of the CWP can significantly improve workers\u2019 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>","DOI":"10.3390\/ijerph19116439","type":"journal-article","created":{"date-parts":[[2022,5,26]],"date-time":"2022-05-26T08:50:22Z","timestamp":1653555022000},"page":"6439","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Computer-Aided Diagnosis of Coal Workers\u2019 Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7606-3719","authenticated-orcid":false,"given":"Liton","family":"Devnath","sequence":"first","affiliation":[{"name":"School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0672-2616","authenticated-orcid":false,"given":"Peter","family":"Summons","sequence":"additional","affiliation":[{"name":"School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suhuai","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dadong","family":"Wang","sequence":"additional","affiliation":[{"name":"Quantitative Imaging, CSIRO Data61, Marsfield, Sydney, NSW 2122, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2174-3383","authenticated-orcid":false,"given":"Kamran","family":"Shaukat","sequence":"additional","affiliation":[{"name":"School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia"},{"name":"Department of Data Science, University of the Punjab, Lahore 54890, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1252-260X","authenticated-orcid":false,"given":"Ibrahim A.","family":"Hameed","sequence":"additional","affiliation":[{"name":"Department of ICT and Natural Sciences, Norwegian University of Science and Technology, 7491 Trondheim, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanan","family":"Aljuaid","sequence":"additional","affiliation":[{"name":"Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU), P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"249","DOI":"10.4104\/pcrj.2013.00055","article-title":"Pneumoconiosis","volume":"22","author":"Cullinan","year":"2013","journal-title":"Prim. Care Respir. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.mpmed.2012.03.011","article-title":"Pneumoconiosis","volume":"40","author":"Fishwick","year":"2012","journal-title":"Medicine"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1289\/ehp.0800144","article-title":"Pneumoconiosis from Agricultural Dust Exposure among Young California Farmworkers","volume":"117","author":"Schenker","year":"2009","journal-title":"Environ. Health Perspect."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1539\/joh.48.309","article-title":"24 Years of Pneumoconiosis Mortality Surveillance in Australia","volume":"48","author":"Smith","year":"2006","journal-title":"J. Occup. Health"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2095","DOI":"10.1016\/S0140-6736(12)61728-0","article-title":"Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: A systematic analysis for the Global Burden of Disease Study 2010","volume":"380","author":"Lozano","year":"2012","journal-title":"Lancet"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"414","DOI":"10.5694\/mja16.00357","article-title":"Coal workers\u2019 pneumoconiosis: An Australian perspective","volume":"204","author":"Zosky","year":"2016","journal-title":"Med. J. Aust."},{"key":"ref_7","first-page":"1","article-title":"Current Review of Pneumoconiosis Among US Coal Miners","volume":"2019","author":"Hall","year":"2019","journal-title":"Curr. Environ. Health Rep."},{"key":"ref_8","first-page":"675","article-title":"Silicosis and coal workers\u2019 pneumoconiosis","volume":"108","author":"Castranova","year":"2000","journal-title":"Environ. Health Perspect."},{"key":"ref_9","first-page":"71","article-title":"Coal Mine Respirable Dust Control View project Dust control with canopy air curtain View project M\u0131n\u0131ng eng\u0131neer\u0131ng Coal workers\u2019 pneumoconiosis prevalence disparity between Australia and the United States","volume":"64","author":"Joy","year":"2012","journal-title":"Min. Eng. Mag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.compmedimag.2007.02.002","article-title":"Computer-Aided Diagnosis in Medical Imaging: Historical Review, Current Status and Future Potential","volume":"31","author":"Doi","year":"2007","journal-title":"Comput. Med Imaging Graph."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, Q., and Nishikawa, R.M. (2015). Computer-Aided Detection and Diagnosis in Medical Imaging, Taylor & Francis.","DOI":"10.1201\/b18191"},{"key":"ref_12","first-page":"1","article-title":"Computer-Aided Detection and Diagnosis in Medical Imaging","volume":"2013","author":"Chen","year":"2013","journal-title":"Comput. Math. Methods Med."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Halalli, B., and Makandar, A. (2018). Computer Aided Diagnosis\u2014Medical Image Analysis Techniques. Breast Imaging, InTech.","DOI":"10.5772\/intechopen.69792"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Han, L., Gao, Q., Yang, J., Wu, Q., Zhu, B., Zhang, H., Ding, B., and Ni, C. (2017). Survival Analysis of Coal Workers\u2019 Pneumoconiosis (CWP) Patients in a State-Owned Mine in the East of China from 1963 to 2014. Int. J. Environ. Res. Public Health, 14.","DOI":"10.3390\/ijerph14050489"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1264711","DOI":"10.1080\/20018525.2017.1264711","article-title":"A systematic review of occupational exposure to coal dust and the risk of interstitial lung diseases","volume":"4","author":"Beer","year":"2017","journal-title":"Eur. Clin. Respir. J."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yang, H.-Y. (2019). Prediction of pneumoconiosis by serum and urinary biomarkers in workers exposed to asbestos-contaminated minerals. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0214808"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.ijheh.2013.03.006","article-title":"Prevalence of coal workers\u2019 pneumoconiosis in China: A systematic analysis of 2001\u20132011 studies","volume":"217","author":"Mo","year":"2013","journal-title":"Int. J. Hyg. Environ. Health"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1136\/thx.53.5.398","article-title":"Coal mining and chronic obstructive pulmonary disease: A review of the evidence","volume":"53","author":"Coggon","year":"1998","journal-title":"Thorax"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1136\/oem.2009.047126","article-title":"Pneumoconiosis among underground bituminous coal miners in the United States: Is silicosis becoming more frequent?","volume":"67","author":"Laney","year":"2010","journal-title":"Occup. Environ. Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1080\/15287390500196537","article-title":"Mechanistically Identified Suitable Biomarkers of Exposure, Effect, and Susceptibility for Silicosis and Coal-Worker\u2019S Pneumoconiosis: A Comprehensive Review","volume":"9","author":"Gulumian","year":"2006","journal-title":"J. Toxicol. Environ. Health Part B"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1104","DOI":"10.1002\/ajim.23184","article-title":"Assessment of pneumoconiosis in surface coal miners after implementation of a national radiographic surveillance program, United States, 2014\u20132019","volume":"63","author":"Hall","year":"2020","journal-title":"Am. J. Ind. Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1016\/j.scitotenv.2018.10.083","article-title":"Mixed-dust pneumoconiosis: Review of diagnostic and classification problems with presentation of a work-related case","volume":"652","author":"Baur","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4","DOI":"10.21106\/ijtmrph.14","article-title":"Measures to control the prevalence of pneumoconiosis in coal mining: A review of the literature","volume":"1","author":"Ayaaba","year":"2017","journal-title":"Int. J. Transl. Med. Res. Public Health"},{"key":"ref_24","first-page":"71","article-title":"Omics Approaches for Exploring Pneumoconiosis: A Review","volume":"34","author":"Luo","year":"2021","journal-title":"Biomed. Environ. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1111\/resp.13034","article-title":"Coal mine dust lung disease in the modern era","volume":"22","author":"Perret","year":"2017","journal-title":"Respirology"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"7856","DOI":"10.3390\/ijerph120707856","article-title":"Prevalence Characteristics of Coal Workers\u2019 Pneumoconiosis (CWP) in a State-Owned Mine in Eastern China","volume":"12","author":"Han","year":"2015","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"ref_27","unstructured":"Jones, J., and Hancox, J. (2020, August 07). Reticular Interstitial Pattern, Radiology Reference Article, Radiopaedia.org. Available online: https:\/\/radiopaedia.org\/articles\/reticular-interstitial-pattern."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/s13244-012-0207-7","article-title":"Mimics in chest disease: Interstitial opacities","volume":"4","author":"Oikonomou","year":"2012","journal-title":"Insights into Imaging"},{"key":"ref_29","unstructured":"Nickson, C. (2020, August 06). Pulmonary Opacities on Chest X-ray, LITFL-CCC Differential Diagnosis. Available online: https:\/\/litfl.com\/pulmonary-opacities-on-chest-x-ray\/."},{"key":"ref_30","unstructured":"(2020, August 07). Occupational Safety and Health Series No. 22 (Rev. 2011), \u201cGuidelines for the use of the ILO International Classification of Radiographs of Pneumoconioses, Revised Edition 2011\u201d. Available online: http:\/\/www.ilo.org\/global\/topics\/safety-and-health-at-work\/resources-library\/publications\/WCMS_168260\/lang--en\/index.htm."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1148\/rg.261055070","article-title":"Pneumoconiosis: Comparison of Imaging and Pathologic Findings","volume":"26","author":"Chong","year":"2006","journal-title":"RadioGraphics"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1539\/joh.L8015","article-title":"The Value of High Resolution Computed Tomography in the Diagnostics of Small Opacities and Complications of Silicosis in Mine Machinery Manufacturing Workers, Compared to Radiography","volume":"50","author":"Sun","year":"2008","journal-title":"J. Occup. Health"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1539\/joh.L10065","article-title":"Effect of a Two-hour Training on Physicians\u2019 Skill in Interpreting Pneumoconiotic Chest Radiographs","volume":"52","author":"Ngatu","year":"2010","journal-title":"J. Occup. Health"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Murray, V., Pattichis, M.S., Davis, H., Barriga, E.S., and Soliz, P. (2009, January 7\u201310). Multiscale AM-FM analysis of pneumoconiosis X-ray images. Proceedings of the International Conference on Image Processing (ICIP), Cairo, Egypt.","DOI":"10.1109\/ICIP.2009.5414522"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1109\/42.993132","article-title":"Automatic detection of abnormalities in chest radiographs using local texture analysis","volume":"21","author":"Katsuragawa","year":"2002","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_36","unstructured":"Kondo, H., and Kouda, T. (2001, January 25\u201328). Detection of pneumoconiosis rounded opacities using neural network. Proceedings of the Annual Conference of the North American Fuzzy Information Processing Society\u2014NAFIPS, Vancouver, BC, Canada."},{"key":"ref_37","first-page":"13","article-title":"Computer-aided diagnosis for pneumoconiosis using neural network","volume":"7","author":"Kondo","year":"2001","journal-title":"Int. J. Biomed. Soft Comput. Hum. Sci. Off. J. Biomed. Fuzzy Syst. Assoc."},{"key":"ref_38","first-page":"453","article-title":"Computer Aided Diagnosis for Pneumoconiosis Radiograps Using Neural Network","volume":"33","author":"Kondo","year":"2000","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0010-4825(75)90018-9","article-title":"A texture analysis method in classification of coal workers\u2019 pneumoconiosis","volume":"5","author":"Ledley","year":"1975","journal-title":"Comput. Biol. Med."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1148\/radiographics.10.2.2326513","article-title":"Quantitative computer-aided analysis of lung texture in chest radiographs","volume":"10","author":"Katsuragawa","year":"1990","journal-title":"RadioGraphics"},{"key":"ref_41","unstructured":"Chen, X., Hasegawa, J.-I., and Toriwaki, J.-I. (November, January 14). Quantitative diagnosis of pneumoconiosis based on recognition of small rounded opacities in chest X-ray images. Proceedings of the International Conference on Pattern Recognition, Rome, Italy."},{"key":"ref_42","unstructured":"Kobatake, H., Oh\u2019Ishi, K., and Miyamichi, J. (1987, January 6\u20139). Automatic diagnosis of pneumoconiosis by texture analysis of chest X-ray images. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing; Institute of Electrical and Electronics Engineers, Dalas, TX, USA."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1109\/TPAMI.1980.6592371","article-title":"Computer-aided recognition of small rounded pneumoconiosis opacities in chest X-rays","volume":"PAMI-2","author":"Savol","year":"1980","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1097\/00004424-197607000-00002","article-title":"Automated computer screening of chest radiographs for pneumoconiosis","volume":"11","author":"Turner","year":"1976","journal-title":"Investig. Radiol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1109\/TC.1976.5009212","article-title":"Measurement of Pneumoconiosis by Computer","volume":"C-25","author":"Jagoe","year":"1976","journal-title":"IEEE Trans. Comput."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1136\/oem.32.4.267","article-title":"Reading chest radiographs for pneumoconiosis by computer","volume":"32","author":"Jagoe","year":"1975","journal-title":"Occup. Environ. Med."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1109\/TBME.1975.324475","article-title":"Computer Classification of Pneumoconiosis from Radiographs of Coal Workers","volume":"BME-22","author":"Hall","year":"1975","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1109\/TSMC.1974.5408519","article-title":"Computer Diagnosis of Pneumoconiosis","volume":"4","author":"Kruger","year":"1974","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_49","unstructured":"Arzhaeva, Y., Wang, D., Devnath, L., Amirgholipour, S.K., McBean, R., Hillhouse, J., Luo, S., Meredith, D., and Newbigin, K. (2019). Development of Automated Diagnostic Tools for Pneumoconiosis Detection from Chest X-ray Radiographs, Coal Services Health and Safety Trust. The Final Report Prepared for Coal Services Health and Safety Trust."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1007\/s10278-017-9942-0","article-title":"Computerized Classification of Pneumoconiosis on Digital Chest Radiography Artificial Neural Network with Three Stages","volume":"30","author":"Okumura","year":"2017","journal-title":"J. Digit. Imaging"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"606","DOI":"10.14738\/jbemi.15.606","article-title":"Application of a Computer-aid Diagnosis of Pneumoconiosis for CR X-ray Images","volume":"1","author":"Abe","year":"2014","journal-title":"J. Biomed. Eng. Med Imaging"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1186\/1475-925X-13-141","article-title":"The development and evaluation of a computerized diagnosis scheme for pneumoconiosis on digital chest radiographs","volume":"13","author":"Zhu","year":"2014","journal-title":"Biomed. Eng. Online"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s12194-013-0255-9","article-title":"Development of CAD based on ANN analysis of power spectra for pneumoconiosis in chest radiographs: Effect of three new enhancement methods","volume":"7","author":"Okumura","year":"2014","journal-title":"Radiol. Phys. Technol."},{"key":"ref_54","first-page":"24","article-title":"Computer-Aided Diagnosis of Pneumoconiosis X-ray Images Scanned with a Common CCD Scanner","volume":"1","author":"Abe","year":"2013","journal-title":"Autom. Control Intell. Syst."},{"key":"ref_55","first-page":"244","article-title":"Computer-aided diagnosis for pneumoconiosis based on texture analysis on digital chest radiographs","volume":"241","author":"Cai","year":"2013","journal-title":"Appl. Mech. Mater."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1007\/s10278-013-9620-9","article-title":"Support Vector Machine Model for Diagnosing Pneumoconiosis Based on Wavelet Texture Features of Digital Chest Radiographs","volume":"27","author":"Zhu","year":"2013","journal-title":"J. Digit. Imaging"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"336","DOI":"10.6009\/jjrt.67.336","article-title":"Computerized Classification of Pneumoconiosis Radiographs Based on Grey Level Co-occurrence Matrices","volume":"67","author":"Masumoto","year":"2011","journal-title":"Jpn. J. Radiol. Technol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1126","DOI":"10.1007\/s10278-010-9357-7","article-title":"Computerized Analysis of Pneumoconiosis in Digital Chest Radiography: Effect of Artificial Neural Network Trained with Power Spectra","volume":"24","author":"Okumura","year":"2010","journal-title":"J. Digit. Imaging"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1007\/s10278-010-9276-7","article-title":"An Automatic Computer-Aided Detection Scheme for Pneumoconiosis on Digital Chest Radiographs","volume":"24","author":"Yu","year":"2010","journal-title":"J. Digit. Imaging"},{"key":"ref_60","first-page":"147","article-title":"Extraction of Features for Diagnosing Pneumoconiosis from Chest Radiographs Obtained with a CCD Scanner","volume":"8","author":"Nakamura","year":"2010","journal-title":"J. Digit. Inf. Manag."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Sundararajan, R., Xu, H., Annangi, P., Tao, X., Sun, X., and Mao, L. (2010, January 14\u201317). A multiresolution support vector machine based algorithm for pneumoconiosis detection from chest radiographs. Proceedings of the 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Rotterdam, The Netherlands.","DOI":"10.1109\/ISBI.2010.5490239"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Nakamura, M., Abe, K., and Minami, M. (2009, January 4\u20136). Quantitative evaluation of pneumoconiosis in chest radiographs obtained with a CCD scanner. Proceedings of the 2nd International Conference on the Applications of Digital Information and Web Technologies, ICADIWT 2009, London, UK.","DOI":"10.1109\/ICADIWT.2009.5273899"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Yu, P., Zhao, J., Xu, H., Yang, C., Sun, X., Chen, S., and Mao, L. (2009, January 26\u201328). Computer Aided Detection for Pneumoconiosis Based on Histogram Analysis. Proceedings of the 2009 1st International Conference on Information Science and Engineering, ICISE 2009, Nanjing, China.","DOI":"10.1109\/ICISE.2009.415"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Yu, P., Zhao, J., Xu, H., Sun, X., and Mao, L. (2009, January 17\u201319). Computer Aided Detection for Pneumoconiosis Based on Co-Occurrence Matrices Analysis. Proceedings of the 2009 2nd International Conference on Biomedical Engineering and Informatics, Tianjin, China.","DOI":"10.1109\/BMEI.2009.5304924"},{"key":"ref_65","unstructured":"Pattichis, M., Christodoulou, C., James, D., Ketai, L., and Soliz, P. (2002, January 7\u20139). A screening system for the assessment of opacity profusion in chest radiographs of miners with pneumoconiosis. Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, Sante Fe, NM, USA."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Soliz, P., Pattichis, M., Ramachandran, J., and James, D.S. (2001, January 17\u201322). Computer-assisted diagnosis of chest radiographs for pneumoconioses. Proceedings of the Medical Imaging 2001: Image Processing, San Diago, CA, USA.","DOI":"10.1117\/12.431143"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2201","DOI":"10.1038\/s41598-020-77924-z","article-title":"A deep learning-based model for screening and staging pneumoconiosis","volume":"11","author":"Zhang","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"104125","DOI":"10.1016\/j.compbiomed.2020.104125","article-title":"Automated detection of pneumoconiosis with multilevel deep features learned from chest X-Ray radiographs","volume":"129","author":"Devnath","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Wang, D., Arzhaeva, Y., Devnath, L., Qiao, M., Amirgholipour, S., Liao, Q., McBean, R., Hillhouse, J., Luo, S., and Meredith, D. (December, January 29). Automated Pneumoconiosis Detection on Chest X-Rays Using Cascaded Learning with Real and Synthetic Radiographs. Proceedings of the 2020 Digital Image Computing: Techniques and Applications (DICTA), Melbourne, Australia.","DOI":"10.1109\/DICTA51227.2020.9363416"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1136\/oemed-2019-106386","article-title":"Potential of deep learning in assessing pneumoconiosis depicted on digital chest radiography","volume":"77","author":"Wang","year":"2020","journal-title":"Occup. Environ. Med."},{"key":"ref_71","unstructured":"Devnath, L., Luo, S., Summons, P., and Wang, D. Performance Comparison of Deep Learning Models for Black Lung Detection on Chest X-ray Radiographs. Proceedings of the ACM International Conference Proceeding Series."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Devnath, L., Luo, S., Summons, P., and Wang, D. (2019, January 2\u20134). An accurate black lung detection using transfer learning based on deep neural networks. Proceedings of the International Conference Image and Vision Computing New Zealand, Dunedin, New Zealand.","DOI":"10.1109\/IVCNZ48456.2019.8960961"},{"key":"ref_73","first-page":"647","article-title":"An Improved CNN-Based Pneumoconiosis Diagnosis Method on X-ray Chest Film","volume":"Volume 11956 LNCS","author":"Zheng","year":"2019","journal-title":"Proceedings of the Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNCS"},{"key":"ref_74","unstructured":"Wirth, M.A. (2004). Texture Analysis, University of Guelph."},{"key":"ref_75","first-page":"1","article-title":"Texture image analysis and texture classification methods\u2014A review","volume":"2","author":"Armi","year":"2019","journal-title":"Int. Online J. Image Process. Pattern Recognit."},{"key":"ref_76","unstructured":"Haralick, R.M. (1986). Statistical image texture analysis. Handbook of Pattern Recognition and Image Processing, Academic Press."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Tomita, F., and Tsuji, S. (1990). Statistical Texture Analysis. Computer Analysis of Visual Textures, Springer.","DOI":"10.1007\/978-1-4613-1553-7"},{"key":"ref_78","first-page":"4268","article-title":"Statistical Texture Analysis","volume":"2","author":"Srinivasan","year":"2008","journal-title":"Int. J. Comput. Inf. Eng."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/0031-3203(91)90052-7","article-title":"Texture features based on texture spectrum","volume":"24","author":"He","year":"1991","journal-title":"Pattern Recognit."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Hu, X., and Ensor, A. (2018, January 19\u201321). Fourier Spectrum Image Texture Analysis. Proceedings of the International Conference Image and Vision Computing New Zealand, Auckland, New Zealand.","DOI":"10.1109\/IVCNZ.2018.8634740"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"6","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man. Cybern."},{"key":"ref_83","unstructured":"Monash Centre for Occupational and Environmental Health (2016). Review of Respiratory Component of the Coal Mine Workers\u2019 Health Scheme for the Queensland Department of Natural Resources and Mines Final Report, Monash University."},{"key":"ref_84","unstructured":"(2020, July 02). Black Lung Case Confirmed in NSW\u2014Australian Mining. Available online: https:\/\/www.australianmining.com.au\/news\/black-lung-case-confirmed-in-nsw\/."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1550","DOI":"10.5858\/arpa.2010-0393-OA","article-title":"The Influence of Dust Standards on the Prevalence and Severity of Coal Worker\u2019s Pneumoconiosis at Autopsy in the United States of America","volume":"135","author":"Vallyathan","year":"2011","journal-title":"Arch. Pathol. Lab. Med."},{"key":"ref_86","unstructured":"Materka, A., and Strzelecki, M. (1998). Texture Analysis Methods\u2014A Review, Technical University of Lodz, Institute of Electronics. COST B11 Report."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Agwu, K.K., and Ohagwu, C.C. (2016). Histogram-Based Texture Characterization and Classification of Brain Tissues in Non-Contrast CT Images of Stroke Patients. Pattern Recognition\u2014Analysis and Applications, InTech.","DOI":"10.5772\/65349"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1109\/83.585239","article-title":"Image enhancement based on a nonlinear multiscale method","volume":"6","author":"Sattar","year":"1997","journal-title":"IEEE Trans. Image Process."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/0167-8655(92)90056-6","article-title":"Multiscale color image enhancement","volume":"13","author":"Toet","year":"1992","journal-title":"Pattern Recognit. Lett."},{"key":"ref_90","first-page":"22","article-title":"Wavelet-based Texture Analysis","volume":"1","author":"Scheunders","year":"1997","journal-title":"Int. J. Comput. Sci. Inf. Manag."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.1016\/S0167-8655(02)00390-2","article-title":"Texture classification using wavelet transform","volume":"24","author":"Arivazhagan","year":"2003","journal-title":"Pattern Recognit. Lett."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Livens, S. (1997, January 14\u201317). Wavelets for texture analysis, an overview. Proceedings of the Sixth International Conference on Image Processing and Its Applications, Dublin, Ireland.","DOI":"10.1049\/cp:19970958"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1109\/83.242353","article-title":"Texture analysis and classification with tree-structured wavelet transform","volume":"2","author":"Chang","year":"1993","journal-title":"IEEE Trans. Image Process."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"15","DOI":"10.5391\/IJFIS.2002.2.1.015","article-title":"Automatic Detection of Interstitial Lung Disease using Neural Network","volume":"2","author":"Kouda","year":"2002","journal-title":"Int. J. Fuzzy Log. Intell. Syst."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_96","unstructured":"Goodfellow, L., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_97","unstructured":"Nielsen, M. (2015). Neural Networks and Deep Learning, Determination Press."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"110358","DOI":"10.1109\/ACCESS.2019.2933670","article-title":"Ensemble Learners of Multiple Deep CNNs for Pulmonary Nodules Classification Using CT Images","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Shih, F.Y. (2010). Image Processing and Pattern Recognition: Fundamentals and Techniques, John Wiley and Sons.","DOI":"10.1002\/9780470590416"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going Deeper with Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_102","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the Advances in Neural Information Processing Systems 25 (NIPS 2012), Stateline, NV, USA."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"He, K. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_104","unstructured":"Chollet, F. (2020, April 24). Building Powerful Image Classification Models Using Very Little Data, Keras Blog. Available online: http:\/\/deeplearning.lipingyang.org\/wp-content\/uploads\/2016\/12\/Building-powerful-image-classification-models-using-very-little-data.pdf."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Zhu, J., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., and Liu, C. (2018, January 4\u20137). A Survey on Deep Transfer Learning. Proceedings of the 27th International Conference on Artificial Neural Networks, Rhodes, Greece.","DOI":"10.1007\/978-3-030-01424-7_27"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A Survey on Transfer Learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Torrey, L., and Shavlik, J. (2010). Transfer Learning: Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, IGI Global.","DOI":"10.4018\/978-1-60566-766-9.ch011"},{"key":"ref_109","unstructured":"Simonyan, K., and Zisserman, A. (2020, April 26). Very Deep Convolutional Networks for Large-Scale Image Recognition, Published as a Conference Paper at ICLR. Available online: https:\/\/arxiv.org\/abs\/1409.1556."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_112","unstructured":"Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., and Shpanskaya, K. (2017). CheXNet: Radiologist-Level Pneumonia Detection on Chest X-rays with Deep Learning. arXiv."}],"container-title":["International Journal of Environmental Research and Public Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1660-4601\/19\/11\/6439\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:18:41Z","timestamp":1760138321000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1660-4601\/19\/11\/6439"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,25]]},"references-count":112,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["ijerph19116439"],"URL":"https:\/\/doi.org\/10.3390\/ijerph19116439","relation":{},"ISSN":["1660-4601"],"issn-type":[{"value":"1660-4601","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,25]]}}}