{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T21:22:07Z","timestamp":1791667327119,"version":"4.3.4"},"reference-count":38,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,4,6]],"date-time":"2022-04-06T00:00:00Z","timestamp":1649203200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Education (MOE, Korea) through the fostering project of 'Soonchunhyang University and Industry Cooperation Complex' supervised by the Korea Institute for Advancement of Technology (KIAT): None","award":["BK21 FOUR (Fostering Outstanding Universities for Research): No. :5199990914048;  Soonchunhyang University Research Fund: None"],"award-info":[{"award-number":["BK21 FOUR (Fostering Outstanding Universities for Research): No. :5199990914048;  Soonchunhyang University Research Fund: None"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Chest X-ray radiographic (CXR) imagery enables earlier and easier lung disease diagnosis. Therefore, in this paper, we propose a deep learning method using a transfer learning technique to classify lung diseases on CXR images to improve the efficiency and accuracy of computer-aided diagnostic systems\u2019 (CADs\u2019) diagnostic performance. Our proposed method is a one-step, end-to-end learning, which means that raw CXR images are directly inputted into a deep learning model (EfficientNet v2-M) to extract their meaningful features in identifying disease categories. We experimented using our proposed method on three classes of normal, pneumonia, and pneumothorax of the U.S. National Institutes of Health (NIH) data set, and achieved validation performances of loss = 0.6933, accuracy = 82.15%, sensitivity = 81.40%, and specificity = 91.65%. We also experimented on the Cheonan Soonchunhyang University Hospital (SCH) data set on four classes of normal, pneumonia, pneumothorax, and tuberculosis, and achieved validation performances of loss = 0.7658, accuracy = 82.20%, sensitivity = 81.40%, and specificity = 94.48%; testing accuracy of normal, pneumonia, pneumothorax, and tuberculosis classes was 63.60%, 82.30%, 82.80%, and 89.90%, respectively.<\/jats:p>","DOI":"10.3390\/diagnostics12040915","type":"journal-article","created":{"date-parts":[[2022,4,6]],"date-time":"2022-04-06T16:21:30Z","timestamp":1649262090000},"page":"915","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":125,"title":["Deep Learning in Multi-Class Lung Diseases\u2019 Classification on Chest X-ray Images"],"prefix":"10.3390","volume":"12","author":[{"given":"Sungyeup","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Software Convergence, Soonchunhyang University, Asan 31538, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1232-0610","authenticated-orcid":false,"given":"Beanbonyka","family":"Rim","sequence":"additional","affiliation":[{"name":"Department of Software Convergence, Soonchunhyang University, Asan 31538, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seongjun","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology-Head and Neck Surgery, Cheonan Hospital, Soonchunhyang University College of Medicine, Cheonan 31151, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7467-3038","authenticated-orcid":false,"given":"Ahyoung","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Kennesaw State University, Marietta, GA 30144, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9112-2673","authenticated-orcid":false,"given":"Sedong","family":"Min","sequence":"additional","affiliation":[{"name":"Department of Medical IT Engineering, Soonchunhyang University, Asan 31538, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9963-5521","authenticated-orcid":false,"given":"Min","family":"Hong","sequence":"additional","affiliation":[{"name":"Department of Computer Software Engineering, Soonchunhyang University, Asan 31538, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,6]]},"reference":[{"key":"ref_1","unstructured":"Forum of International Respiratory Societies (FIRS) (2021, September 01). 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