{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T00:20:23Z","timestamp":1774138823883,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T00:00:00Z","timestamp":1624233600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T00:00:00Z","timestamp":1624233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100006261","name":"Taif University","doi-asserted-by":"publisher","award":["TURSP-2020\/79"],"award-info":[{"award-number":["TURSP-2020\/79"]}],"id":[{"id":"10.13039\/501100006261","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Computing"],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1007\/s00607-021-00971-5","type":"journal-article","created":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T15:03:21Z","timestamp":1624287801000},"page":"887-908","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["IoT-enabled stacked ensemble of deep neural networks for the diagnosis of COVID-19 using chest CT scans"],"prefix":"10.1007","volume":"105","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8050-8431","authenticated-orcid":false,"given":"Mohammad","family":"Shorfuzzaman","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,6,21]]},"reference":[{"key":"971_CR1","doi-asserted-by":"publisher","unstructured":"Abdulsalam Y, Hossain MS (2020) COVID-19 networking demand: an auction-based mechanism for automated selection of edge computing services. IEEE Trans Netw Sci Eng. https:\/\/doi.org\/10.1109\/TNSE.2020.3026637","DOI":"10.1109\/TNSE.2020.3026637"},{"key":"971_CR2","doi-asserted-by":"crossref","unstructured":"Alamri A, et al (2014) Evaluating the impact of a cloud-based serious game on obese people. Comput Hum Behav 30:468\u2013475.","DOI":"10.1016\/j.chb.2013.06.021"},{"key":"971_CR3","doi-asserted-by":"publisher","first-page":"100003","DOI":"10.1016\/j.bea.2021.100003","volume":"1","author":"E Benmalek","year":"2021","unstructured":"Benmalek E, Elmhamdi J, Jilbab A (2021) Comparing CT scan and chest X-ray imaging for COVID-19 diagnosis. Biomed Eng Adv. 1:100003. https:\/\/doi.org\/10.1016\/j.bea.2021.100003","journal-title":"Biomed Eng Adv."},{"key":"971_CR4","doi-asserted-by":"crossref","unstructured":"Bernheim A, Mei X, Huang M, Yang Y, Fayad ZA, Zhang N, Diao K, Lin B, Zhu X, Li K et al (2020) Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection. Radiology, pp 200463","DOI":"10.1148\/radiol.2020200463"},{"key":"971_CR5","doi-asserted-by":"publisher","first-page":"19196","DOI":"10.1038\/s41598-020-76282-0","volume":"10","author":"J Chen","year":"2020","unstructured":"Chen J, Wu L, Zhang J et al (2020) (2020) Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography. Nature Sci Rep 10:19196. https:\/\/doi.org\/10.1038\/s41598-020-76282-0","journal-title":"Nature Sci Rep"},{"issue":"1","key":"971_CR6","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1148\/radiol.2020200230","volume":"295","author":"M Chung","year":"2020","unstructured":"Chung M, Bernheim A, Mei X et al (2020) CT imaging features of 2019 novel coronavirus (2019-ncov). Radiology 295(1):202\u2013207","journal-title":"Radiology"},{"key":"971_CR7","unstructured":"COVID-19 dashboard, coronaBoard, URL: https:\/\/coronaboard.com\/. Accessed Dec 07, 2020"},{"key":"971_CR8","doi-asserted-by":"publisher","first-page":"4080","DOI":"10.1038\/s41467-020-17971-2","volume":"11","author":"SA Harmon","year":"2020","unstructured":"Harmon SA, Sanford TH, Xu S et al (2020) Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets. Nat Commun 11:4080. https:\/\/doi.org\/10.1038\/s41467-020-17971-2","journal-title":"Nat Commun"},{"issue":"5","key":"971_CR9","doi-asserted-by":"publisher","first-page":"517","DOI":"10.3390\/e22050517","volume":"22","author":"AM Hasan","year":"2020","unstructured":"Hasan AM, Al-Jawad MM, Jalab HA et al (2020) Classification of COVID-19 coronavirus, pneumonia and healthy lungs in CT scans using Q-Deformed entropy and deep learning features. Entropy 22(5):517","journal-title":"Entropy"},{"key":"971_CR10","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"971_CR11","doi-asserted-by":"publisher","first-page":"149808","DOI":"10.1109\/ACCESS.2020.3016780","volume":"8","author":"MJ Horry","year":"2020","unstructured":"Horry MJ et al (2020) COVID-19 detection through transfer learning using multimodal imaging data. IEEE Access 8:149808\u2013149824","journal-title":"IEEE Access"},{"issue":"4","key":"971_CR12","doi-asserted-by":"publisher","first-page":"2399","DOI":"10.1109\/JIOT.2017.2772959","volume":"5","author":"MS Hossain","year":"2018","unstructured":"Hossain MS, Muhammad G (2018) Emotion-aware connected healthcare big data towards 5G. IEEE Internet Things J 5(4):2399\u20132406","journal-title":"IEEE Internet Things J"},{"issue":"4","key":"971_CR13","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1109\/MNET.011.2000458","volume":"34","author":"MS Hossain","year":"2020","unstructured":"Hossain MS, Muhammad G, Guizani N (2020) Explainable AI and mass surveillance system-based healthcare framework to combat COVID-I9 like pandemics. IEEE Netw 34(4):126\u2013132","journal-title":"IEEE Netw"},{"issue":"6","key":"971_CR14","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/MWC.2015.7368826","volume":"22","author":"L Hu","year":"2015","unstructured":"Hu L, Qiu M, Song J, Hossain MS, Ghoneim A (2015) Software defined healthcare networks. IEEE Wireless Commun 22(6):67\u201375","journal-title":"IEEE Wireless Commun"},{"issue":"10223","key":"971_CR15","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 et al (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet 395(10223):497\u2013506","journal-title":"The Lancet"},{"key":"971_CR16","doi-asserted-by":"publisher","first-page":"100412","DOI":"10.1016\/j.imu.2020.100412","volume":"20","author":"MZ Islam","year":"2020","unstructured":"Islam MZ, Islam MM, Asraf A (2020) A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. Inf Med Unlocked 20:100412. https:\/\/doi.org\/10.1016\/j.imu.2020.100412","journal-title":"Inf Med Unlocked"},{"key":"971_CR17","first-page":"1","volume":"2020","author":"Y Li","year":"2020","unstructured":"Li Y, Xia L (2020) Coronavirus disease 2019 (COVID-19): role of chest CT in diagnosis and management. Am J Roentgenol 2020:1\u20137","journal-title":"Am J Roentgenol"},{"key":"971_CR18","doi-asserted-by":"publisher","unstructured":"Lin H et al (2020) Privacy-enhanced data fusion for COVID-19 applications in intelligent Internet of edical things. IEEE Internet Things Journal. https:\/\/doi.org\/10.1109\/JIOT.2020.3033129","DOI":"10.1109\/JIOT.2020.3033129"},{"key":"971_CR19","unstructured":"Li L, Qin L, Xu Z et al (2020) Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology: 200905"},{"key":"971_CR20","doi-asserted-by":"publisher","unstructured":"Liu S et al (2018) 3D anisotropic hybrid network: Transferring convolutional features from 2D images to 3D anisotropic volumes. In: Frangi A, Schnabel J, Davatzikos C, Alberola-L\u00f3pez C, Fichtinger G (eds) Medical image computing and computer assisted intervention\u2014MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science, vol 11071. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-00934-2_94","DOI":"10.1007\/978-3-030-00934-2_94"},{"key":"971_CR21","doi-asserted-by":"publisher","first-page":"136046","DOI":"10.1109\/ACCESS.2020.3011123","volume":"8","author":"Z Long","year":"2020","unstructured":"Long Z, Alharthi R, Saddik AE (2020) NeedFull\u2014a tweet analysis platform to study human needs during the COVID-19 pandemic in New York state. IEEE Access 8:136046\u2013136055","journal-title":"IEEE Access"},{"key":"971_CR22","doi-asserted-by":"crossref","unstructured":"Masud M, Hossain MS, Alamri A (2012) Data Interoperability and Multimedia Content Management in e-Health Systems. IEEE Trans Inf Technol Biomed 16(6):1015\u20131023.","DOI":"10.1109\/TITB.2012.2202244"},{"key":"971_CR23","doi-asserted-by":"publisher","unstructured":"Mishra AK, Das SK, Roy P, Bandyopadhyay S (2020) Identifying COVID19 from chest CT images: A deep convolutional neural networks based approach. J Healthcare Eng, 8843664. https:\/\/doi.org\/10.1155\/2020\/8843664","DOI":"10.1155\/2020\/8843664"},{"key":"971_CR24","doi-asserted-by":"crossref","unstructured":"Hossain MS, Muhammad G (2019) Emotion recognition using deep learning approach from audio-visual emotional big data. Inf Fusion 49:69\u201378","DOI":"10.1016\/j.inffus.2018.09.008"},{"key":"971_CR25","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.inffus.2021.02.013","volume":"72","author":"G Muhammad","year":"2021","unstructured":"Muhammad G, Hossain MS (2021) COVID-19 and non-COVID-19 classification using multi-layers fusion from lung ultrasound images. Inf Fusion 72:80\u201388","journal-title":"Inf Fusion"},{"issue":"2","key":"971_CR26","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1109\/MNET.011.2000326","volume":"35","author":"G Muhammad","year":"2021","unstructured":"Muhammad G, Hossain MS (2021) A deep learning-based edge-centric COVID-19-like pandemic screening and diagnosis system within a B5G framework using blockchain. IEEE Netw 35(2):74\u201381","journal-title":"IEEE Netw"},{"issue":"2","key":"971_CR27","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1109\/JSAC.2020.3020654","volume":"39","author":"G Muhammad","year":"2021","unstructured":"Muhammad G, Hossain MS, Kumar N (2021) EEG-based pathology detection for home health monitoring. IEEE J Select Areas Commun 39(2):603\u2013610","journal-title":"IEEE J Select Areas Commun"},{"issue":"8","key":"971_CR28","doi-asserted-by":"publisher","first-page":"2595","DOI":"10.1109\/TMI.2020.2995508","volume":"39","author":"X Ouyang","year":"2020","unstructured":"Ouyang X, Huo J, Xia L et al (2020) Dual-sampling attention network for diagnosis of COVID-19 from community acquired pneumonia. IEEE Trans Medical Imag 39(8):2595\u20132605","journal-title":"IEEE Trans Medical Imag"},{"key":"971_CR29","doi-asserted-by":"publisher","unstructured":"Rahman MA, Hossain MS (2020) An Internet of medical things-enabled edge computing framework for tackling COVID-19. IEEE Internet Things J. https:\/\/doi.org\/10.1109\/JIOT.2021.3051080","DOI":"10.1109\/JIOT.2021.3051080"},{"issue":"4","key":"971_CR30","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1109\/MNET.011.2000353","volume":"31","author":"MA Rahman","year":"2020","unstructured":"Rahman MA et al (2020) B5G and explainable deep learning assisted healthcare vertical at the edge: COVID-I9 perspective. IEEE Netw 31(4):98\u2013105","journal-title":"IEEE Netw"},{"key":"971_CR31","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1145\/3421725","volume":"17","author":"MA Rahman","year":"2021","unstructured":"Rahman MA et al (2021) A multimodal, multimedia point-of-care deep learning framework for COVID-19 diagnosis. ACM Trans Multimedia Comput Commun Appl 17:24","journal-title":"ACM Trans Multimedia Comput Commun Appl"},{"issue":"2","key":"971_CR32","first-page":"39","volume":"14","author":"AE Saddik","year":"2019","unstructured":"Saddik AE, Badawi H, Velazquez R, Laamart F et al (2019) Dtwins: a digital twins ecosystem for health and well-being. IEEE COMSOC MMTC Commun Front 14(2):39\u201346","journal-title":"IEEE COMSOC MMTC Commun Front"},{"key":"971_CR33","doi-asserted-by":"publisher","first-page":"87","DOI":"10.2214\/AJR.20.23034","volume":"215","author":"S Salehi","year":"2020","unstructured":"Salehi S, Abedi A, Balakrishnan S, Gholamrezanezhad A (2020) Coronavirus disease 2019 (COVID-19): a systematic review of imaging findings in 919 patients. Am J Roentgenol 215:87\u201393","journal-title":"Am J Roentgenol"},{"key":"971_CR34","unstructured":"SARS-COV-2 Ct-Scan Dataset, https:\/\/www.kaggle.com\/plameneduardo\/sarscov2-ctscan-dataset. Accessed 05 Sep 2020"},{"issue":"4","key":"971_CR35","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1016\/S1473-3099(20)30086-4","volume":"20","author":"H Shi","year":"2020","unstructured":"Shi H, Han X, Jiang N et al (2020) Radiological findings from 81 patients with covid-19 pneumonia in Wuhan, China: a descriptive study. Lancet Inf Dis 20(4):425\u2013434","journal-title":"Lancet Inf Dis"},{"key":"971_CR36","doi-asserted-by":"publisher","first-page":"107700","DOI":"10.1016\/j.patcog.2020.107700","volume":"113","author":"M Shorfuzzaman","year":"2021","unstructured":"Shorfuzzaman M (2021) Hossain MS (2021) MetaCOVID: a Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients. Pattern Recog 113:107700","journal-title":"Pattern Recog"},{"key":"971_CR37","first-page":"1359","volume":"3","author":"M Shorfuzzaman","year":"2020","unstructured":"Shorfuzzaman M, Masud M (2020) On the detection of COVID-19 from chest X-ray images using CNN-based transfer learning. Comput Mater Contin 3:1359\u20131381","journal-title":"Comput Mater Contin"},{"key":"971_CR38","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: 3rd International conference on learning representations (ICLR 2015)"},{"key":"971_CR39","unstructured":"Statement on the second meeting of the international health regulations (2005) In: Emergency committee regarding the outbreak of novel coronavirus (2019-nCoV). World Health Organization. 30 Jan 2020. Archived from the original on 31 Jan 2020. Accessed 10 Aug 2020"},{"key":"971_CR40","doi-asserted-by":"publisher","unstructured":"Sverzellati N, Ryerson CJ , Milanese G et al (2021) Chest x-ray or CT for COVID-19 pneumonia? Comparative study in a simulated triage setting. Eur Resp J 57(5). https:\/\/doi.org\/10.1183\/13993003.04188-2020","DOI":"10.1183\/13993003.04188-2020"},{"key":"971_CR41","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y et al (2015) Goingd deeper with convolutions. In: Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"971_CR42","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp. 2818-2826","DOI":"10.1109\/CVPR.2016.308"},{"issue":"1","key":"971_CR43","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1613\/jair.594","volume":"10","author":"K Ting","year":"1999","unstructured":"Ting K, Witten IH (1999) Issues in stacked generalization. J Artif Intell Res 10(1):271\u2013289","journal-title":"J Artif Intell Res"},{"issue":"2605","key":"971_CR44","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(2605):2579\u20132605","journal-title":"J Mach Learn Res"},{"key":"971_CR45","first-page":"19549","volume":"10","author":"L Wang","year":"2020","unstructured":"Wang L, Lin ZQ, Wong A (2020) COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci Rep Nature 10:19549","journal-title":"Sci Rep Nature"},{"issue":"2","key":"971_CR46","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","volume":"5","author":"DH Wolpert","year":"1992","unstructured":"Wolpert DH (1992) Stacked generalization. Neural Netw 5(2):241\u2013259","journal-title":"Neural Netw"},{"issue":"10","key":"971_CR47","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 et al (2020) A deep learning system to screen coronavirus disease 2019 pneumonia. Engineering 6(10):1122\u20131129","journal-title":"Engineering"},{"key":"971_CR48","doi-asserted-by":"publisher","first-page":"69273","DOI":"10.1109\/ACCESS.2020.2987281","volume":"8","author":"Y Zhou","year":"2020","unstructured":"Zhou Y, Dong H, Saddik AE (2020) Deep learning in next-frame prediction: a benchmark review. IEEE Access 8:69273\u201369283","journal-title":"IEEE Access"},{"key":"971_CR49","doi-asserted-by":"crossref","unstructured":"Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep Learn Med Image Anal Multimodal Learn Clin Decision Support, pp 3\u201311","DOI":"10.1007\/978-3-030-00889-5_1"}],"container-title":["Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-021-00971-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00607-021-00971-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-021-00971-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T01:56:53Z","timestamp":1679882213000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00607-021-00971-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,21]]},"references-count":49,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["971"],"URL":"https:\/\/doi.org\/10.1007\/s00607-021-00971-5","relation":{},"ISSN":["0010-485X","1436-5057"],"issn-type":[{"value":"0010-485X","type":"print"},{"value":"1436-5057","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,21]]},"assertion":[{"value":"23 April 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 June 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 June 2021","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 interest.","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"}]}}