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This study aimed to develop a computer-aided diagnostic system for the screening and staging of pneumoconiosis based on a multi-stage joint deep learning approach using X-ray chest radiographs of pneumoconiosis patients.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>In this study, a total of 498 medical chest radiographs were obtained from the Department of Radiology of West China Fourth Hospital. The dataset was randomly divided into a training set and a test set at a ratio of 4:1. Following histogram equalization for image enhancement, the images were segmented using the U-Net model, and staging was predicted using a convolutional neural network classification model. We first used Efficient-Net for multi-classification staging diagnosis, but the results showed that stage I\/II of pneumoconiosis was difficult to diagnose. Therefore, based on clinical practice we continued to improve the model by using the Res-Net 34 Multi-stage joint method.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Of the 498 cases collected, the classification model using the Efficient-Net achieved an accuracy of 83% with a Quadratic Weighted Kappa (QWK) score of 0.889. The classification model using the multi-stage joint approach of Res-Net 34 achieved an accuracy of 89% with an area under the curve (AUC) of 0.98 and a high QWK score of 0.94.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>In this study, the diagnostic accuracy of pneumoconiosis staging was significantly improved by an innovative combined multi-stage approach, which provided a reference for clinical application and pneumoconiosis screening.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12880-024-01337-x","type":"journal-article","created":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T14:03:41Z","timestamp":1719929021000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Deep learning pneumoconiosis staging and diagnosis system based on multi-stage joint approach"],"prefix":"10.1186","volume":"24","author":[{"given":"Chang","family":"Liu","sequence":"first","affiliation":[]},{"given":"Yeqi","family":"Fang","sequence":"additional","affiliation":[]},{"given":"YuHuan","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Li","sequence":"additional","affiliation":[]},{"given":"Dongsheng","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,2]]},"reference":[{"key":"1337_CR1","unstructured":"Global, regional, and, national, incidence, prevalence, and, years, lived, with, disability, for, 354, diseases, and, injuries, for, 195, countries, and, territories, 1990\u20132017:, a, systematic, analysis, for, the, Global, Burden, of, Disease, Study, 2017. 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The study was retrospective. The use of patient information will not adversely affect them, so we waived informed consent.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics statement and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"165"}}