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In order to concentrate the feature extraction ability of the model more on the lung region and restrain the influence of external background factors, a two-stage pipeline from coarse to fine was established. First, the U-Net model was used to extract the lung regions on each sides of the collection images. Second, the ResNet-34 model with transfer learning strategy was implemented to learn the image features extracted in the lung region to achieve accurate classification of pneumoconiosis patients and normal people.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Among the 1760 cases collected, the accuracy and the area under curve of the classification model were 92.46% and 89% respectively.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The successful application of deep learning in the diagnosis of pneumoconiosis further demonstrates the potential of medical artificial intelligence and proves the effectiveness of our proposed algorithm. However, when we further classified pneumoconiosis patients and normal subjects into four categories, we found that the overall accuracy decreased to 70.1%. We will use the CT modality in future studies to provide more details of lung regions.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12880-021-00723-z","type":"journal-article","created":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T16:04:01Z","timestamp":1638979441000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Pneumoconiosis computer aided diagnosis system based on X-rays and deep learning"],"prefix":"10.1186","volume":"21","author":[{"given":"Fan","family":"Yang","sequence":"first","affiliation":[]},{"given":"Zhi-Ri","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Min","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Shengchun","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Wanyin","family":"Qi","sequence":"additional","affiliation":[]},{"given":"Chong","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Yuanyuan","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Yinan","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Zekuan","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,12,8]]},"reference":[{"issue":"2","key":"723_CR1","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1136\/oemed-2020-106610","volume":"78","author":"T Wang","year":"2021","unstructured":"Wang T, Sun W, Wu H, Cheng Y, Li Y, Meng F, Ni C. 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Ethical approval was obtained from the Ethical Committee of the hospital (KY2020200). The institutional review board approved this retrospective study and waived the need to obtain informed consent from patients.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declares that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"189"}}