{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T10:58:46Z","timestamp":1768820326697,"version":"3.49.0"},"reference-count":35,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T00:00:00Z","timestamp":1680652800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"crossref","award":["MOST 109-2410-H-006-116-MY2, MOST110-2511-H-006-013-MY3"],"award-info":[{"award-number":["MOST 109-2410-H-006-116-MY2, MOST110-2511-H-006-013-MY3"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Higher Education Sprout Project, Ministry of Education to the Headquarters of University Advancement at National Cheng Kung University"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Sen. Netw."],"published-print":{"date-parts":[[2023,8,31]]},"abstract":"<jats:p>\n            It is worth noting that this 21st century has experienced so many economic, social, cultural and political turbulences throughout the world. The 2019 novel coronavirus (COVID-19) outbreak has been regarded by the\n            <jats:bold>World Health Organization (WHO)<\/jats:bold>\n            as a public health crisis of global concern. Nowadays, the\n            <jats:bold>chest X-ray (CXR)<\/jats:bold>\n            and\n            <jats:bold>chest computed tomography (CT)<\/jats:bold>\n            are a more effective imaging technique for diagnosing lung related problems. Deep learning has been more mature in the field of supervised learning, but other areas of machine learning have just started, especially for the areas of unsupervised learning and reinforcement learning. Deep learning has very good performance in speech recognition and image recognition. Using deep learning approaches to diagnose COVID-19 can achieve better cures and treatments. This research presents the data augmentation and L2 regularization approach for transfer learning in several state-of-the-art deep learning models such as VGG16, VGG19, ResNet, and AlexNet, with\n            <jats:bold>Convolutional Block Attention Module (CBAM)<\/jats:bold>\n            to perform binary classification ( such as normal and COVID-19\/pneumonia cases or COVID-19 and pneumonia cases) and also multi-class classification (such as COVID-19, pneumonia, and normal cases) of covid-chestxray-dataset and NIH datasets. In addition, the performance evaluation adopted the confused matrix to evaluate the results of these models. To sum up, the CBAM can improve the accuracy of all deep learning models to achieve better performance in contrast to that without this architecture. The findings can be a reference for the related COVID-19 diagnosis researches especially during the post-pandemic era.\n          <\/jats:p>","DOI":"10.1145\/3558098","type":"journal-article","created":{"date-parts":[[2022,8,25]],"date-time":"2022-08-25T11:35:22Z","timestamp":1661427322000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["COVID-19 Diagnosis System Based on Chest X-ray Images Using Optimized Convolutional Neural Network"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3945-4363","authenticated-orcid":false,"given":"Mu-Yen","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Engineering Science, National Cheng Kung University and Center for Innovative FinTech Business Models, National Cheng Kung University, Tainan City, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7871-0454","authenticated-orcid":false,"given":"Po-Ru","family":"Chiang","sequence":"additional","affiliation":[{"name":"Department of Engineering Science, National Cheng Kung University, Tainan City, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,4,5]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"2021 WHO: Coronavirus (COVID-19) data https:\/\/www.who.int\/data\/. [Online; accessed August 20 2021]"},{"key":"e_1_3_1_3_2","unstructured":"2020 WHO: Who timeline - Covid-19. https:\/\/www.who.int\/news-room\/detail\/08-04-2020-who-timeline\u2014covid-19. [Online; accessed August 20 2021]"},{"key":"e_1_3_1_4_2","unstructured":"WHO: WHO announces COVID-19 outbreak a pandemic. https:\/\/www.euro.who.int\/en\/health-topics\/health-emergencies\/coronavirus-covid-19\/news\/news\/2020\/3\/who-announces-covid-19-outbreak-a-pandemic [Online; accessed August 20 2021]"},{"key":"e_1_3_1_5_2","unstructured":"2021 CDC: Guidance for SARS-CoV-2 Point-of-Care and Rapid Testing. https:\/\/www.cdc.gov\/coronavirus\/2019-ncov\/lab\/point-of-care-testing.html. [Online; accessed August 20 2021]"},{"key":"e_1_3_1_6_2","unstructured":"2021 WHO: Testing strategies for COVID-19. https:\/\/www.who.int\/docs\/default-source\/coronaviruse\/risk-comms-updates\/updates46-testing-strategies.pdf?sfvrsn=c9401268_6. [Online; accessed August 20 2021]"},{"key":"e_1_3_1_7_2","unstructured":"2021 CDC: CDC 2019-Novel Coronavirus (2019-nCoV) Real-Time RT-PCR Diagnostic Panel. https:\/\/www.who.int\/docs\/default-source\/coronaviruse\/whoinhouseassays.pdf. [Online; accessed August 20 2021]"},{"key":"e_1_3_1_8_2","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky A.","year":"2012","unstructured":"A. Krizhevsky, I. Sutskever, and G. E. Hinton. 2012. ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25 (2012), 1097\u20131105.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_1_11_2","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan K.","year":"2014","unstructured":"K. Simonyan and A. Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.","journal-title":"arXiv preprint"},{"key":"e_1_3_1_12_2","unstructured":"2021 WHO: Coronavirus disease (COVID-19) advice for the public. https:\/\/www.who.int\/emergencies\/diseases\/novel-coronavirus-2019\/advice-for-public. [Online; accessed August 20 2021]"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.m2426"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-020-01900-3"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"e_1_3_1_16_2","article-title":"Corona virus (COVID-19) Classification using CT Images by Machine Learning Methods","author":"Barstugan M.","year":"2020","unstructured":"M. Barstugan, U. Ozkaya, and S. Ozturk. 2020. Corona virus (COVID-19) Classification using CT Images by Machine Learning Methods. arXiv preprint arXiv:2003.09424, 2020.","journal-title":"arXiv preprint"},{"key":"e_1_3_1_17_2","doi-asserted-by":"crossref","unstructured":"L. Yan H. T. Zhang J. Goncalves Y. Xiao M. Wang Y. Guo ... and X. Huang. 2020. A machine learning-based model for survival prediction in patients with severe COVID-19 infection. medRxiv. DOI:https:\/\/doi.org\/10.1101\/2020.02.27.20028027.","DOI":"10.1101\/2020.02.27.20028027"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.3390\/a13100249"},{"key":"e_1_3_1_19_2","doi-asserted-by":"crossref","unstructured":"X. Qi Z. Jiang Q. Yu C. Shao H. Zhang H. Yue ... and S. Huang. 2020. Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with ARS-CoV-2 infection: A multicenter study. medRxiv. DOI:https:\/\/doi.org\/10.1101\/2020.02.29.20029603","DOI":"10.1101\/2020.02.29.20029603"},{"key":"e_1_3_1_20_2","article-title":"CovidX-Net: A framework of deep learning classifiers to diagnose Covid-19 in x-ray images","author":"Hemdan E. E.","year":"2020","unstructured":"E. E. Hemdan, M. A. Shouman, and M. E. Karar. 2020. CovidX-Net: A framework of deep learning classifiers to diagnose Covid-19 in x-ray images. arXiv preprint arXiv:2003.11055.","journal-title":"arXiv preprint"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102365"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbe.2020.08.008"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110495"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110245"},{"key":"e_1_3_1_25_2","article-title":"Detection of Corona virus (COVID-19) associated pneumonia based on generative adversarial networks and a fine-tuned deep transfer learning model using Chest X-ray Dataset","author":"Khalifa N. E. M.","year":"2020","unstructured":"N. E. M. Khalifa, M. H. N. Taha, A. E. Hassanien, and S. Elghamrawy. 2020. Detection of Corona virus (COVID-19) associated pneumonia based on generative adversarial networks and a fine-tuned deep transfer learning model using Chest X-ray Dataset. arXiv preprint arXiv:2004.01184.","journal-title":"arXiv preprint"},{"key":"e_1_3_1_26_2","doi-asserted-by":"crossref","unstructured":"S. Dutta and S. K. Bandyopadhyay. 2020. Machine learning approach for confirmation of COVID-19 Cases: Positive negative death and release. medRxiv. DOI:https:\/\/doi.org\/10.1101\/2020.03.25.20043505","DOI":"10.1101\/2020.03.25.20043505"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3012595"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3099165"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph18063056"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare9050522"},{"key":"e_1_3_1_31_2","unstructured":"2020 SegmentedLungCXRs dataset. https:\/\/github.com\/mhorry\/SegmentedLungCXRs. [Online; accessed August 20 2021]"},{"key":"e_1_3_1_32_2","doi-asserted-by":"crossref","unstructured":"J. P. Cohen P. Morrison L. Dao K. Roth T. Q. Duong and M. Ghassemi. 2020. COVID-19 image data collection: Prospective predictions are the future. arXiv:2006.11988.","DOI":"10.59275\/j.melba.2020-48g7"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.369"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00344251"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.4.541"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_1"}],"container-title":["ACM Transactions on Sensor Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3558098","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3558098","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:49:32Z","timestamp":1750182572000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3558098"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,5]]},"references-count":35,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,8,31]]}},"alternative-id":["10.1145\/3558098"],"URL":"https:\/\/doi.org\/10.1145\/3558098","relation":{},"ISSN":["1550-4859","1550-4867"],"issn-type":[{"value":"1550-4859","type":"print"},{"value":"1550-4867","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,5]]},"assertion":[{"value":"2021-11-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-08-15","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-04-05","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}