{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T10:09:56Z","timestamp":1784110196721,"version":"3.55.0"},"reference-count":28,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,23]],"date-time":"2023-04-23T00:00:00Z","timestamp":1682208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Education (MOE, Korea)","award":["20221042"],"award-info":[{"award-number":["20221042"]}]},{"name":"Soonchunhyang University Research Fund","award":["20221042"],"award-info":[{"award-number":["20221042"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>The deep learning approach has recently attracted much attention for its outstanding performance to assist in clinical diagnostic tasks, notably in computer-aided solutions. Computer-aided solutions are being developed using chest radiography to identify lung diseases. A chest X-ray image is one of the most often utilized diagnostic imaging modalities in computer-aided solutions since it produces non-invasive standard-of-care data. However, the accurate identification of a specific illness in chest X-ray images still poses a challenge due to their high inter-class similarities and low intra-class variant abnormalities, especially given the complex nature of radiographs and the complex anatomy of the chest. In this paper, we proposed a deep-learning-based solution to classify four lung diseases (pneumonia, pneumothorax, tuberculosis, and lung cancer) and healthy lungs using chest X-ray images. In order to achieve a high performance, the EfficientNet B7 model with the pre-trained weights of ImageNet trained by Noisy Student was used as a backbone model, followed by our proposed fine-tuned layers and hyperparameters. Our study achieved an average test accuracy of 97.42%, sensitivity of 95.93%, and specificity of 99.05%. Additionally, our findings were utilized as diagnostic supporting software in OView-AI system (computer-aided application). We conducted 910 clinical trials and achieved an AUC confidence interval (95% CI) of the diagnostic results in the OView-AI system of 97.01%, sensitivity of 95.68%, and specificity of 99.34%.<\/jats:p>","DOI":"10.3390\/diagnostics13091519","type":"journal-article","created":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T01:35:44Z","timestamp":1682300144000},"page":"1519","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["OView-AI Supporter for Classifying Pneumonia, Pneumothorax, Tuberculosis, Lung Cancer Chest X-ray Images Using Multi-Stage Superpixels Classification"],"prefix":"10.3390","volume":"13","author":[{"given":"Joonho","family":"Oh","sequence":"first","affiliation":[{"name":"Department of Mechanical System Engineering, Chosun University, Gwangju 61452, Republic of Korea"},{"name":"OTOM, Co., Ltd., Gwangju 61042, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chanho","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Radiology, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongchang","family":"Lee","sequence":"additional","affiliation":[{"name":"Haewootech Co., Ltd., Busan 46742, Republic of 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":"OTOM, Co., Ltd., Gwangju 61042, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Younggyu","family":"Kim","sequence":"additional","affiliation":[{"name":"OTOM, Co., Ltd., Gwangju 61042, Republic of 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, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiwon","family":"Lyu","sequence":"additional","affiliation":[{"name":"Division of Respiratory Medicine, Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suha","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Nursing, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Republic of 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, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,23]]},"reference":[{"key":"ref_1","unstructured":"Lung Diseases (2022, November 28). National Institute of Environmental Health Sciences, Available online: https:\/\/www.niehs.nih.gov\/health\/topics\/conditions\/lung-disease\/index.cfm."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"105490","DOI":"10.1016\/j.engappai.2022.105490","article-title":"Recent Advancements in Deep Learning based Lung Cancer Detection: A Systematic Review","volume":"116","author":"Dodia","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_3","unstructured":"Pneumonia System and Diagnosis (2022, November 28). American Lung Association. Available online: https:\/\/www.lung.org\/lung-health-diseases\/lung-disease-lookup\/pneumonia\/symptoms-and-diagnosis."},{"key":"ref_4","unstructured":"Tuberculosis System and Diagnosis (2022, November 28). American Lung Association. Available online: https:\/\/www.lung.org\/lung-health-diseases\/lung-disease-lookup\/tuberculosis\/symptoms-diagnosis."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"89","DOI":"10.4046\/trd.2020.0134","article-title":"Recent Trends of Lung Cancer in Korea","volume":"84","author":"Lee","year":"2021","journal-title":"Tuberc. Respir. Dis."},{"key":"ref_6","unstructured":"Symptoms, Diagnosis and Treating Pneumothorax (2022, November 28). American Lung Association. Available online: https:\/\/www.lung.org\/lung-health-diseases\/lung-disease-lookup\/pneumothorax\/symptoms-diagnosis-treatment."},{"key":"ref_7","first-page":"1104","article-title":"X-ray image segmentation using multi-task learning","volume":"14","author":"Park","year":"2020","journal-title":"KSII Trans. Internet Inf. Syst."},{"key":"ref_8","first-page":"154","article-title":"Advancement of Deep Learning in Pneumonia\/COVID-19 Classification and Localization: A Systematic Review with Qualitative and Quantitative Analysis","volume":"8","author":"Shah","year":"2022","journal-title":"Chronic Dis. Transl. Med."},{"key":"ref_9","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). Efficientnet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xie, Q., Luong, M.T., Hovy, E., and Le, Q.V. (2020, January 14\u201319). Self-training with Noisy Student Improves ImageNet Classification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR42600.2020.01070"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Asif, S., Wenhui, Y., Amjad, K., Jin, H., Tao, Y., and Jinhai, S. (Expert Syst., 2022). Detection of COVID-19 from Chest X-ray Images: Boosting the Performance with Convolutional Neural Network and Transfer Learning, Expert Syst., Online ahead of print.","DOI":"10.1111\/exsy.13099"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"101004","DOI":"10.1016\/j.imu.2022.101004","article-title":"Development and Integration of VGG and Dense Transfer-learning Systems Supported with Diverse Lung Images for Discovery of the Coronavirus Identity","volume":"32","author":"Hamwi","year":"2022","journal-title":"Inform. Med. Unlocked"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hong, M., Rim, B., Lee, H., Jang, H., Oh, J., and Choi, S. (2021). Multi-class Classification of Lung Diseases using CNN Models. Appl. Sci., 11.","DOI":"10.3390\/app11199289"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3213","DOI":"10.1002\/mp.15582","article-title":"A Radiomics-boosted Deep-learning Model for COVID-19 and non-COVID-19 Pneumonia Classification using Chest X-ray Images","volume":"49","author":"Hu","year":"2022","journal-title":"Med. Phys."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kim, S., Rim, B., Choi, S., Lee, A., Min, S., and Hong, M. (2022). Deep Learning in Multi-Class Lung Diseases\u2019 Classification on Chest X-ray Images. Diagnostics, 12.","DOI":"10.3390\/diagnostics12040915"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"13855","DOI":"10.1007\/s11042-022-13843-7","article-title":"CDC_Net: Multi-classification Convolutional Neural Network Model for Detection of COVID-19, Pneumothorax, Pneumonia, Lung Cancer, and Tuberculosis using Chest X-rays","volume":"82","author":"Malik","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"102008","DOI":"10.1016\/j.compmedimag.2021.102008","article-title":"DenResCov-19: A Deep Transfer Learning Network for Robust Automatic Classification of COVID-19, Pneumonia, and Tuberculosis from X-rays","volume":"94","author":"Mamalakis","year":"2021","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Shamrat, F.J.M., Azam, S., Karim, A., Islam, R., Tasnim, Z., Ghosh, P., and De Boer, F. (2022). LungNet22: A Fine-Tuned Model for Multiclass Classification and Prediction of Lung Disease Using X-ray Images. J. Pers. Med., 12.","DOI":"10.3390\/jpm12050680"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"118","DOI":"10.5114\/pjr.2022.113435","article-title":"Deep Learning-based Automatic Detection of Tuberculosis Disease in Chest X-ray Images","volume":"87","author":"Showkatian","year":"2022","journal-title":"Pol. J. Radiol."},{"key":"ref_20","first-page":"2131","article-title":"A Tuberculosis Detection Method Using Attention and Sparse R-CNN","volume":"16","author":"Xu","year":"2022","journal-title":"KSII Trans. Internet Inf. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"623","DOI":"10.2214\/AJR.20.24801","article-title":"Performance of a severity score on admission chest radiograph in predicting clinical outcomes in hospitalized patients with coronavirus disease (COVID-19)","volume":"217","author":"Reeves","year":"2021","journal-title":"Am. J. Roentgenol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1136\/thx.2010.136242","article-title":"A simple, valid, numerical score for grading chest X-ray severity in adult smear-positive pulmonary tuberculosis","volume":"65","author":"Ralph","year":"2010","journal-title":"Thorax"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.jtcvs.2017.08.138","article-title":"The eighth edition TNM stage classification for lung cancer: What does it mean on main street?","volume":"155","author":"Detterbeck","year":"2018","journal-title":"J. Thorac. Cardiovasc. Surg."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.jbi.2014.02.013","article-title":"Sample size estimation in diagnostic test studies of biomedical informatics","volume":"48","year":"2014","journal-title":"J. Biomed. Inform."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1118","DOI":"10.1373\/clinchem.2004.031823","article-title":"ROC curves in clinical chemistry: Uses, misuses, and possible solutions","volume":"50","author":"Obuchowski","year":"2004","journal-title":"Clin. Chem."},{"key":"ref_26","unstructured":"Zhang, M.R., Lucas, J., Hinton, G., and Ba, J. (2019). Lookahead Optimizer: K Steps Forward, 1 Step Back. arXiv."},{"key":"ref_27","unstructured":"Dozat, T. (2015, January 7\u20139). Technical Report, Incorporating Nesterov Momentum into Adam. Proceedings of the ICLR Workshop, San Diego, CA, USA."},{"key":"ref_28","unstructured":"M\u00fcller, R., Kornblith, S., and Hinton, G. (2019). When Does Label Smoothing Help?. arXiv."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/9\/1519\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:21:55Z","timestamp":1760124115000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/9\/1519"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,23]]},"references-count":28,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["diagnostics13091519"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13091519","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,23]]}}}