{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:50:06Z","timestamp":1781110206078,"version":"3.54.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,2,3]],"date-time":"2022-02-03T00:00:00Z","timestamp":1643846400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,2,3]],"date-time":"2022-02-03T00:00:00Z","timestamp":1643846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"published-print":{"date-parts":[[2022,3]]},"DOI":"10.1007\/s42979-022-01035-x","type":"journal-article","created":{"date-parts":[[2022,2,3]],"date-time":"2022-02-03T08:03:24Z","timestamp":1643875404000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["CoviLearn: A Machine Learning Integrated Smart X-Ray Device in Healthcare Cyber-Physical System for Automatic Initial Screening of COVID-19"],"prefix":"10.1007","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3450-9767","authenticated-orcid":false,"given":"Debanjan","family":"Das","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sagnik","family":"Ghosal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saraju P.","family":"Mohanty","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,2,3]]},"reference":[{"issue":"10223","key":"1035_CR1","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1016\/S0140-6736(20)30185-9","volume":"395","author":"C Wang","year":"2020","unstructured":"Wang C, Horby PW, Hayden FG, Gao GF. A novel coronavirus outbreak of global health concern. Lancet. 2020;395(10223):470\u20133.","journal-title":"Lancet"},{"key":"1035_CR2","doi-asserted-by":"crossref","unstructured":"Liu T,\u00a0Hu J,\u00a0Kang M,\u00a0Lin L,\u00a0Zhong H,\u00a0Xiao J,\u00a0He G,\u00a0Song T,\u00a0Huang Q,\u00a0Rong Z, Deng A,\u00a0Zeng W,\u00a0Tan X,\u00a0Zeng S,\u00a0Zhu Z,\u00a0Li J,\u00a0Wan D,\u00a0Lu J,\u00a0Deng H,\u00a0He J,\u00a0Ma W. Transmission dynamics of 2019 novel coronavirus (2019-ncov). 2020.","DOI":"10.2139\/ssrn.3526307"},{"issue":"10223","key":"1035_CR3","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1016\/S0140-6736(20)30183-5","volume":"395","author":"C Huang","year":"2020","unstructured":"Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, Cheng Z, Yu T, Xia J, Wei Y, Wu W, Xie X, Yin W, Li H, Liu M, Xiao Y, Gao H, Guo L, Xie J, Wang G, Jiang R, Gao Z, Jin Q, Wang J, Cao B. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497\u2013506.","journal-title":"Lancet"},{"key":"1035_CR4","doi-asserted-by":"crossref","unstructured":"Challen R,\u00a0Brooks-Pollock E, Read JM,\u00a0Dyson L,\u00a0Tsaneva-Atanasova K,\u00a0Danon L. Risk of mortality in patients infected with sars-cov-2 variant of concern 202012\/1: matched cohort study. BMJ. 2021;372\u201381.","DOI":"10.1101\/2021.02.09.21250937"},{"key":"1035_CR5","doi-asserted-by":"crossref","unstructured":"Jiang F,\u00a0Deng L,\u00a0Zhang L,\u00a0Cai Y, Cheung CW,\u00a0Xia Z. Review of the clinical characteristics of coronavirus disease 2019 (COVID-19). J Gen Int Med. 2020;80(6):656\u201365.","DOI":"10.1007\/s11606-020-05762-w"},{"key":"1035_CR6","first-page":"2020","volume":"2020030300","author":"PK Sethy","year":"2020","unstructured":"Sethy PK, Behera SK. Detection of coronavirus disease (COVID-19) based on deep features. Preprints. 2020;2020030300:2020.","journal-title":"Preprints"},{"issue":"2","key":"1035_CR7","doi-asserted-by":"publisher","first-page":"e200152","DOI":"10.1148\/ryct.2020200152","volume":"2","author":"S Simpson","year":"2020","unstructured":"Simpson S, Kay FU, Abbara S, Bhalla S, Chung JH, Chung M, Henry TS, Kanne JP, Kligerman S, Ko JP, Litt H. Radiological society of North America expert consensus statement on reporting chest CT findings related to COVID-19. Endorsed by the society of thoracic radiology, the American college of radiology, and RSNA. Radiol Cardiothorac Imaging. 2020;2(2):e200152.","journal-title":"Radiol Cardiothorac Imaging."},{"key":"1035_CR8","doi-asserted-by":"crossref","unstructured":"Pattrapisetwong P,\u00a0Chiracharit W. Automatic lung segmentation in chest radiographs using shadow filter and multilevel thresholding. In: Proceedings of ICSEC. 2016;pp. 1\u20136.","DOI":"10.1109\/ICSEC.2016.7859887"},{"issue":"1","key":"1035_CR9","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1186\/s12938-018-0544-y","volume":"17","author":"C Qin","year":"2018","unstructured":"Qin C, Yao D, Shi Y, Song Z. Computer-aided detection in chest radiography based on artificial intelligence: a survey. Biomed Eng Online. 2018;17(1):113.","journal-title":"Biomed Eng Online"},{"issue":"1","key":"1035_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-42557-4","volume":"9","author":"F Pasa","year":"2019","unstructured":"Pasa F, Golkov V, Pfeiffer F, Cremers D, Pfeiffer D. Efficient deep network architectures for fast chest X-ray tuberculosis screening and visualization. Sci Rep. 2019;9(1):1\u20139.","journal-title":"Sci Rep"},{"key":"1035_CR11","doi-asserted-by":"crossref","unstructured":"Ausawalaithong W,\u00a0Thirach A,\u00a0Marukatat S,\u00a0Wilaiprasitporn T. Automatic lung cancer prediction from chest X-ray images using the deep learning approach. In: Proceedings of the international conference on biomedical engineering, 2018;pp. 1\u20135.","DOI":"10.1109\/BMEiCON.2018.8609997"},{"issue":"3","key":"1035_CR12","doi-asserted-by":"publisher","first-page":"e0229963","DOI":"10.1371\/journal.pone.0229963","volume":"15","author":"I Drozdov","year":"2020","unstructured":"Drozdov I, Forbes D, Szubert B, Hall M, Carlin C, Lowe DJ. Supervised and unsupervised language modelling in chest X-ray radiological reports. PLoS ONE. 2020;15(3):e0229963.","journal-title":"PLoS ONE"},{"key":"1035_CR13","unstructured":"Shan F,\u00a0Gao Y,\u00a0Wang J,\u00a0Shi W,\u00a0Shi N,\u00a0Han M,\u00a0Xue Z,\u00a0Shi Y. Lung infection quantification of COVID-19 in CT images with deep learning. arXiv preprint arXiv:2003.04655. 2020."},{"issue":"2","key":"1035_CR14","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1109\/MCE.2020.3040513","volume":"10","author":"H Thapliyal","year":"2021","unstructured":"Thapliyal H, Michael K, Mohanty SP, Srinivas M, Ganapathiraju MK. Consumer technology-based solutions for COVID-19. IEEE Consumer Electron Mag. 2021;10(2):64\u20135.","journal-title":"IEEE Consumer Electron Mag."},{"issue":"1","key":"1035_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-56847-4","volume":"10","author":"L Wang","year":"2020","unstructured":"Wang L, Lin ZQ, Wong A. Covid-net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest x-ray images. Sci Rep. 2020;10(1):1\u201312.","journal-title":"Sci Rep"},{"key":"1035_CR16","doi-asserted-by":"crossref","unstructured":"Li L,\u00a0Qin L,\u00a0Xu Z,\u00a0Yin Y,\u00a0Wang X,\u00a0Kong B,\u00a0Bai J,\u00a0Lu Y,\u00a0Fang Z,\u00a0Song Q,\u00a0Cao K,\u00a0Liu D, Wang G,\u00a0Xu Q,\u00a0Fang X,\u00a0Zhang S,\u00a0Xia J,\u00a0Xia J. Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest ct. Radiology. 2020;296;E65\u201371.","DOI":"10.1148\/radiol.2020200905"},{"key":"1035_CR17","unstructured":"Gozes O,\u00a0Frid-Adar M,\u00a0Greenspan H, Browning P.\u00a0D,\u00a0Zhang H,\u00a0Ji W,\u00a0Bernheim A, Siegel E. Rapid ai development cycle for the coronavirus (COVID-19) pandemic: Initial results for automated detection & patient monitoring using deep learning CT image analysis. arXiv preprint arXiv:2003.05037, 2020."},{"issue":"10","key":"1035_CR18","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.eng.2020.04.010","volume":"6","author":"X Xu","year":"2020","unstructured":"Xu X, Jiang X, Ma C, Du P, Li X, Lv S, Yu L, Ni Q, Chen Y, Su J, Lang G, Li Y, Zhao H, Liu J, Xu K, Ruan L, Sheng J, Qiu Y, Wu W, Liang T, Li L. A deep learning system to screen novel coronavirus disease 2019 pneumonia. Engineering. 2020;6(10):1122\u20139.","journal-title":"Engineering"},{"key":"1035_CR19","unstructured":"Ghoshal B,\u00a0Tucker A. Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection. arXiv preprint arXiv:2003.10769, 2020."},{"key":"1035_CR20","doi-asserted-by":"crossref","unstructured":"Wang S,\u00a0Kang B,\u00a0Ma J,\u00a0Zeng X,\u00a0Xiao M,\u00a0Guo J,\u00a0Cai M,\u00a0Yang J,\u00a0Li Y,\u00a0Meng X,\u00a0Xu B. A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). Eur Radiol 2021;31:6096\u2013104.","DOI":"10.1007\/s00330-021-07715-1"},{"key":"1035_CR21","doi-asserted-by":"publisher","first-page":"102096","DOI":"10.1016\/j.media.2021.102096","volume":"72","author":"C Fang","year":"2021","unstructured":"Fang C, Bai S, Chen Q, Zhou Y, Xia L, Qin L, Gong S, Xie X, Zhou C, Tu D, Zhang C, Liu X, Chen W, Bai X, Torr PH. Deep learning for predicting COVID-19 malignant progression. Med Image Anal. 2021;72:102096.","journal-title":"Med Image Anal"},{"key":"1035_CR22","doi-asserted-by":"crossref","unstructured":"Jin C,\u00a0Chen W,\u00a0Cao Y,\u00a0Xu Z,\u00a0Tan Z,\u00a0Zhang X,\u00a0Deng L,\u00a0Zheng C,\u00a0Zhou J,\u00a0Shi H,\u00a0Feng J. Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nat Commun. 2020;11:5088.","DOI":"10.1038\/s41467-020-18685-1"},{"key":"1035_CR23","doi-asserted-by":"crossref","unstructured":"Jin S,\u00a0Wang B,\u00a0Xu H,\u00a0Luo C,\u00a0Wei L,\u00a0Zhao W,\u00a0Hou X,\u00a0Ma W,\u00a0Xu Z,\u00a0Zheng Z,\u00a0Sun W, Lan L,\u00a0Zhang W,\u00a0Mu X,\u00a0Shi C,\u00a0Wang Z,\u00a0Lee J,\u00a0Jin Z,\u00a0Lin M,\u00a0Jin H,\u00a0Zhang L,\u00a0Guo J,\u00a0Zhao B,\u00a0Ren Z,\u00a0Wang S, You Z,\u00a0Dong J,.\u00a0Wang X,\u00a0Wang J,\u00a0Xu W. Ai-assisted CT imaging analysis for COVID-19 screening: building and deploying a medical ai system in four weeks. Appl. Soft Comput. 2021;98:106897.","DOI":"10.1016\/j.asoc.2020.106897"},{"key":"1035_CR24","doi-asserted-by":"crossref","unstructured":"Narin A,\u00a0Kaya C,\u00a0Pamuk Z. Automatic detection of coronavirus disease (COVID-19) using x-ray images and deep convolutional neural networks. Pattern Anal Appl. 2021;24:1207\u201320.","DOI":"10.1007\/s10044-021-00984-y"},{"key":"1035_CR25","doi-asserted-by":"publisher","first-page":"132665","DOI":"10.1109\/ACCESS.2020.3010287","volume":"8","author":"MEH Chowdhury","year":"2020","unstructured":"Chowdhury MEH, Rahman T, Khandakar A, Mazhar R, Kadir MA, Mahbub ZB, Islam KR, Khan MS, Iqbal A, Emadi NA, Reaz MBI, Islam MT. Can AI help in screening viral and COVID-19 pneumonia? IEEE Access. 2020;8:132665\u201376.","journal-title":"IEEE Access."},{"key":"1035_CR26","doi-asserted-by":"crossref","unstructured":"Maghdid HS, Asaad AT, Ghafoor KZ, Sadiq AS,\u00a0Mirjalili S, Khan MK. Diagnosing COVID-19 pneumonia from x-ray and CT images using deep learning and transfer learning algorithms. In: Multimodal Image Exploitation and Learning 2021. vol. 11734. International Society for Optics and Photonics, Bellingham. 2021;p. 117340E.","DOI":"10.1117\/12.2588672"},{"key":"1035_CR27","unstructured":"NIH. Nih clinical center\u2014cxr8. 2020. https:\/\/nihcc.app.box.com\/v\/ChestXray-NIHCC. Accessed 2 Sept 2017."},{"issue":"1","key":"1035_CR28","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning. J Big Data. 2019;6(1):60.","journal-title":"J. Big Data"},{"key":"1035_CR29","doi-asserted-by":"crossref","unstructured":"He K,\u00a0Zhang X,\u00a0Ren S,\u00a0Sun J. Deep residual learning for image recognition. In: Proceedings of IEEE conference on computer vision pattern recognition. 2016; pp. 770\u20138.","DOI":"10.1109\/CVPR.2016.90"},{"key":"1035_CR30","doi-asserted-by":"crossref","unstructured":"Huang G,\u00a0Liu Z,\u00a0Van Der\u00a0Maaten L, Weinberger K.\u00a0Q. Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2017; pp. 4700\u20134708.","DOI":"10.1109\/CVPR.2017.243"},{"key":"1035_CR31","doi-asserted-by":"crossref","unstructured":"Ozturk T,\u00a0Talo M, Yildirim EA, Baloglu UB,\u00a0Yildirim O, Acharya UR. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med. 2020;121:103792.","DOI":"10.1016\/j.compbiomed.2020.103792"},{"key":"1035_CR32","doi-asserted-by":"crossref","unstructured":"Khatri A,\u00a0Jain R,\u00a0Vashista H,\u00a0Mittal N,\u00a0Ranjan P,\u00a0Janardhanan R. Pneumonia identification in chest X-Ray images using EMD. Trends Commun Cloud Big Data. 2020;99:87\u201398.","DOI":"10.1007\/978-981-15-1624-5_9"},{"key":"1035_CR33","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.irbm.2019.10.006","volume":"41","author":"M To\u011fa\u00e7ar","year":"2020","unstructured":"To\u011fa\u00e7ar M, Ergen B, C\u00f6mert Z, \u00d6zyurt F. A deep feature learning model for pneumonia detection applying a combination of mrmr feature selection and machine learning models. Irbm. 2020;41:212\u201322.","journal-title":"Irbm"},{"issue":"5","key":"1035_CR34","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1007\/s42979-021-00746-x","volume":"2","author":"SLT Vangipuram","year":"2021","unstructured":"Vangipuram SLT, Mohanty SP, Kougianos E. CoviChain: a blockchain based framework for nonrepudiable contact tracing in healthcare cyber-physical systems during pandemic outbreaks. SN Comput Sci. 2021;2(5):346. https:\/\/doi.org\/10.1007\/s42979-021-00746-x (Online).","journal-title":"SN Comput Sci"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-022-01035-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-022-01035-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-022-01035-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,18]],"date-time":"2022-03-18T11:25:15Z","timestamp":1647602715000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-022-01035-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,3]]},"references-count":34,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["1035"],"URL":"https:\/\/doi.org\/10.1007\/s42979-022-01035-x","relation":{},"ISSN":["2662-995X","2661-8907"],"issn-type":[{"value":"2662-995X","type":"print"},{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,3]]},"assertion":[{"value":"2 October 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 January 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 February 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}],"article-number":"150"}}