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Radiol."},{"key":"ref12","author":"Wali","year":"2017","journal-title":"Chest Sonography as an Accurate Diagnostic Tool in the Diagnosis of Patients with Respiratory Distress"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"1472","DOI":"10.1016\/j.acra.2018.02.018","article-title":"Deep learning in radiology","volume":"25","author":"McBee","year":"2018","journal-title":"Acad. Radiol."},{"key":"ref14","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1007\/978-3-319-65981-7_12","article-title":"Deep learning for medical image processing: Overview, challenges and the future","volume":"26","author":"Razzak","year":"2018","journal-title":"Classif. BioApps: Autom. Decis. Making"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-018-1088-1","article-title":"Medical image analysis using convolutional neural networks: A review","volume":"42","author":"Anwar","year":"2018","journal-title":"J. Med. Syst."},{"key":"ref16","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1186\/s13054-023-04426-5","article-title":"A deep learning model enables accurate prediction and quantification of pulmonary edema from chest X-rays","volume":"27","author":"Schulz","year":"2023","journal-title":"Crit. Care"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"48577","DOI":"10.1109\/ACCESS.2022.3172706","article-title":"Deep learning radiographic assessment of pulmonary edema: Optimizing clinical performance, training with serum biomarkers","volume":"10","author":"Huynh","year":"2022","journal-title":"IEEE Access"},{"key":"ref18","series-title":"Int. Conf. Innov. Intell. Syst. Appl. (INISTA)","first-page":"1","article-title":"Classification of COVID-19 and pleural effusion on chest radiographs using CNN fusion","author":"Serte","year":"2021"},{"key":"ref19","series-title":"Int. Conf. Mach. 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Intell."},{"key":"ref22","first-page":"1","author":"Loshchilov","year":"2018","journal-title":"ICLR 2018"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"19549","DOI":"10.1038\/s41598-020-76550-z","article-title":"COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images","volume":"10","author":"Wang","year":"2020","journal-title":"Sci. Rep."},{"key":"ref24","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1097\/RTI.0000000000000512","article-title":"Deep learning localization of pneumonia: 2019 coronavirus (COVID-19) outbreak","volume":"35","author":"Hurt","year":"2020","journal-title":"J. Thorac. 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Eng.: Imaging Vis."},{"key":"ref36","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1148\/radiol.2017162326","article-title":"Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks","volume":"284","author":"Lakhani","year":"2017","journal-title":"Radiology"},{"key":"ref37","unstructured":"National Institutes of Health Clinical Center (NIHCC), \u201cChest X-ray dataset,\u201d 2024. Accessed: Jan. 6, 2024. [Online]. Available: https:\/\/www.kaggle.com\/datasets\/nih-chest-xrays\/data"},{"key":"ref38","unstructured":"Medical Imaging Databank of the Valencia Region (BIMCV), \u201cPadchest,\u201d 2024. Accessed: Jan. 6, 2024. [Online]. Available: https:\/\/github.com\/BIMCV-CSUSP"},{"key":"ref39","unstructured":"Stanfordmlgroup, \u201cCheXpert,\u201d 2024. Accessed: Jan. 15, 2024. [Online]. 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