{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T09:29:02Z","timestamp":1789550942442,"version":"build-2803163510"},"reference-count":83,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T00:00:00Z","timestamp":1662422400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003815","name":"Scientific Research Funds","doi-asserted-by":"publisher","award":["21BS122"],"award-info":[{"award-number":["21BS122"]}],"id":[{"id":"10.13039\/501100003815","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>Pneumoconiosis is a group of occupational lung diseases induced by mineral dust inhalation and subsequent lung tissue reactions. It can eventually cause irreparable lung damage, as well as gradual and permanent physical impairments. It has affected millions of workers in hazardous industries throughout the world, and it is a leading cause of occupational death. It is difficult to diagnose early pneumoconiosis because of the low sensitivity of chest radiographs, the wide variation in interpretation between and among readers, and the scarcity of B-readers, which all add to the difficulty in diagnosing these occupational illnesses. In recent years, deep machine learning algorithms have been extremely successful at classifying and localising abnormality of medical images. In this study, we proposed an ensemble learning approach to improve pneumoconiosis detection in chest X-rays (CXRs) using nine machine learning classifiers and multi-dimensional deep features extracted using CheXNet-121 architecture. There were eight evaluation metrics utilised for each high-level feature set of the associated cross-validation datasets in order to compare the ensemble performance and state-of-the-art techniques from the literature that used the same cross-validation datasets. It is observed that integrated ensemble learning exhibits promising results (92.68% accuracy, 85.66% Matthews correlation coefficient (MCC), and 0.9302 area under the precision\u2013recall (PR) curve), compared to individual CheXNet-121 and other state-of-the-art techniques. Finally, Grad-CAM was used to visualise the learned behaviour of individual dense blocks within CheXNet-121 and their ensembles into three-color channels of CXRs. We compared the Grad-CAM-indicated ROI to the ground-truth ROI using the intersection of the union (IOU) and average-precision (AP) values for each classifier and their ensemble. Through the visualisation of the Grad-CAM within the blue channel, the average IOU passed more than 90% of the pneumoconiosis detection in chest radiographs.<\/jats:p>","DOI":"10.3390\/ijerph191811193","type":"journal-article","created":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T20:52:51Z","timestamp":1662497571000},"page":"11193","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Detection and Visualisation of Pneumoconiosis Using an Ensemble of Multi-Dimensional Deep Features Learned from Chest X-rays"],"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 2308, Australia"},{"name":"British Columbia Cancer Research Centre, Vancouver, BC V5Z 1L3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongwen","family":"Fan","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suhuai","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Information and Physical Sciences, The University of Newcastle, Newcastle 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 2308, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dadong","family":"Wang","sequence":"additional","affiliation":[{"name":"Quantitative Imaging, CSIRO Data61, Marsfield 2122, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Olivas, E.S., Guerrero, J.D.M., Martinez-Sober, M., Magdalena-Benedito, J.R., and Serrano, L. 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