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Current strategies to reduce this problem include early detection and appropriate treatment. Better diagnostic and therapeutic strategies are still needed in poor countries. Artificial-intelligence chest X-ray scheme has the potential to become a screening tool for lower respiratory infection in child. Artificial-intelligence chest X-ray schemes for children are rare and limited to a single lung disease. We need a powerful system as a diagnostic tool for most common lung diseases in children. To address this, we present a computer-aided diagnostic scheme for the chest X-ray images of several common pulmonary diseases of children, including bronchiolitis\/bronchitis, bronchopneumonia\/interstitial pneumonitis, lobar pneumonia, and pneumothorax. The study consists of two main approaches: first, we trained a model based on YOLOv3 architecture for cropping the appropriate location of the lung field automatically. Second, we compared three different methods for multi-classification, included the one-versus-one scheme, the one-versus-all scheme and training a classifier model based on convolutional neural network. Our model demonstrated a good distinguishing ability for these common lung problems in children. Among the three methods, the one-versus-one scheme has the best performance. We could detect whether a chest X-ray image is abnormal with 92.47% accuracy and bronchiolitis\/bronchitis, bronchopneumonia, lobar pneumonia, pneumothorax, or normal with 71.94%, 72.19%, 85.42%, 85.71%, and 80.00% accuracy, respectively. In conclusion, we provide a computer-aided diagnostic scheme by deep learning for common pulmonary diseases in children. This scheme is mostly useful as a screening for normal versus most of lower respiratory problems in children. It can also help review the chest X-ray images interpreted by clinicians and may remind possible negligence. This system can be a good diagnostic assistance under limited medical resources.<\/jats:p>","DOI":"10.1038\/s41598-020-73831-5","type":"journal-article","created":{"date-parts":[[2020,10,15]],"date-time":"2020-10-15T10:04:27Z","timestamp":1602756267000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":62,"title":["Diagnosis of common pulmonary diseases in children by X-ray images and deep learning"],"prefix":"10.1038","volume":"10","author":[{"given":"Kai-Chi","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong-Ren","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei-Shiang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei-Che","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Chen","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hung-Hsun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jyun-Hong","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting-Yi","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang-Ku","family":"Tsai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ti-An","family":"Tsai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chih-Min","family":"Tsai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henry Horng-Shing","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,10,15]]},"reference":[{"key":"73831_CR1","doi-asserted-by":"crossref","unstructured":"Hasan, et al. 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