{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T08:23:57Z","timestamp":1774599837995,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2020,11,9]],"date-time":"2020-11-09T00:00:00Z","timestamp":1604880000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,11,9]],"date-time":"2020-11-09T00:00:00Z","timestamp":1604880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U1836110, 61602253"],"award-info":[{"award-number":["U1836110, 61602253"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Special Foundation by Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET) and Jiangsu Key Laboratory of Big Data Analysis Technolog","award":["No. 2020xtzx005"],"award-info":[{"award-number":["No. 2020xtzx005"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2021,5]]},"DOI":"10.1007\/s10489-020-02002-w","type":"journal-article","created":{"date-parts":[[2020,11,9]],"date-time":"2020-11-09T19:03:44Z","timestamp":1604948624000},"page":"2805-2817","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Stacked-autoencoder-based model for COVID-19 diagnosis on CT images"],"prefix":"10.1007","volume":"51","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8093-7324","authenticated-orcid":false,"given":"Daqiu","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhangjie","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,9]]},"reference":[{"key":"2002_CR1","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1038\/s41564-020-0695-z","volume":"5","author":"AE Gorbalenya","year":"2020","unstructured":"Gorbalenya AE, Baker SC, Baric RS et al (2020) The species Severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nat Microbiol 5:536\u2013544. https:\/\/doi.org\/10.1038\/s41564-020-0695-z","journal-title":"Nat Microbiol"},{"issue":"4","key":"2002_CR2","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1016\/S1672-0229(03)01031-3","volume":"1","author":"KYC Chow","year":"2003","unstructured":"Chow KYC, Raymond KHH et al (2003) Molecular advances in severe acute respiratory syndrome-associated coronavirus (SARS-CoV). Genomics Proteomics Bioinforma 1(4):247\u2013262","journal-title":"Genomics Proteomics Bioinforma"},{"key":"2002_CR3","doi-asserted-by":"publisher","unstructured":"Thompson R (2020) Pandemic potential of 2019-nCoV. Lancet Infect Dis 20(3):261. https:\/\/doi.org\/10.1016\/S1473-3099(20)30068-2","DOI":"10.1016\/S1473-3099(20)30068-2"},{"key":"2002_CR4","doi-asserted-by":"publisher","DOI":"10.1111\/ajt.15876","author":"D Kumar","year":"2020","unstructured":"Kumar D, Oriol M, Yoichiro N et al (2020) COVID-19: A global transplant perspective on successfully navigating a pandemic. Am J Transplant. https:\/\/doi.org\/10.1111\/ajt.15876","journal-title":"Am J Transplant"},{"key":"2002_CR5","doi-asserted-by":"publisher","unstructured":"Subbaraman N (2020) Coronavirus tests: researchers chase new diagnostics to Fight the pandemic. Nature.\u00a0https:\/\/doi.org\/10.1038\/d41586-020-00827-6","DOI":"10.1038\/d41586-020-00827-6"},{"key":"2002_CR6","doi-asserted-by":"publisher","DOI":"10.1093\/cid\/ciaa203","author":"ZJ Shen","year":"2020","unstructured":"Shen ZJ, Yan X, Lu K et al (2020) Genomic diversity of SARS-CoV-2 in coronavirus disease 2019 patients. Clin Infect Dis. https:\/\/doi.org\/10.1093\/cid\/ciaa203","journal-title":"Clin Infect Dis"},{"key":"2002_CR7","doi-asserted-by":"publisher","DOI":"10.1002\/jmv.25762","author":"CT Wang","year":"2020","unstructured":"Wang CT, Liu ZP, Chen ZX et al (2020) The establishment of reference sequence for SARS-CoV-2 and variation analysis. J Med Virol. https:\/\/doi.org\/10.1002\/jmv.25762","journal-title":"J Med Virol"},{"key":"2002_CR8","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1038\/s41423-020-0407-x","volume":"17","author":"P Yang","year":"2020","unstructured":"Yang P, Wang X (2020) COVID-19: a new challenge for human beings. Cell Mol Immunol 17:555\u2013557. https:\/\/doi.org\/10.1038\/s41423-020-0407-x","journal-title":"Cell Mol Immunol"},{"key":"2002_CR9","doi-asserted-by":"publisher","DOI":"10.1021\/acsnano.0c02624","author":"B Udugama","year":"2020","unstructured":"Udugama B, Pranav K, Hannah NK et al (2020) Diagnosing COVID-19: The disease and tools for detection. ACS Nano. https:\/\/doi.org\/10.1021\/acsnano.0c02624","journal-title":"ACS Nano"},{"key":"2002_CR10","doi-asserted-by":"publisher","DOI":"10.1038\/d41586-020-00983-9","author":"A Maxmen","year":"2020","unstructured":"Maxmen A (2020) How poorer countries are scrambling to prevent a coronavirus disaster. Nature. https:\/\/doi.org\/10.1038\/d41586-020-00983-9","journal-title":"Nature"},{"key":"2002_CR11","doi-asserted-by":"crossref","unstructured":"Li Y, Xia LM\u00a0(2020) Coronavirus disease 2019 (COVID-19): role of chest CT in diagnosis and management. Am J Roentgenol :1\u20137","DOI":"10.2214\/AJR.20.22954"},{"key":"2002_CR12","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1038\/s41591-020-0824-5","volume":"26","author":"DSW Ting","year":"2020","unstructured":"Ting DSW, Carin L, Dzau V et al (2020) Digital technology and COVID-19. Nat Med 26:459\u2013461. https:\/\/doi.org\/10.1038\/s41591-020-0824-5","journal-title":"Nat Med"},{"key":"2002_CR13","doi-asserted-by":"publisher","unstructured":"Bai HX, Wang R, Xiong Z et al (n.d.) AI Augmentation of radiologist performance in distinguishing COVID-19 from pneumonia of origin at chest CT. Published Online:Apr 27, 2020. https:\/\/doi.org\/10.1148\/radiol.2020201491","DOI":"10.1148\/radiol.2020201491"},{"key":"2002_CR14","doi-asserted-by":"publisher","unstructured":"Oh Y, Park S, Ye JC (2020) Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets. IEEE Trans Med Imaging 39(8):2688\u20132700.\u00a0https:\/\/doi.org\/10.1109\/TMI.2020.2993291","DOI":"10.1109\/TMI.2020.2993291"},{"key":"2002_CR15","doi-asserted-by":"publisher","unstructured":"Hernandez-Matamoros A, Fujita H, Hayashi T, Perez-Meana H (2020) Forecasting of COVID19 per regions using ARIMA models and polynomial functions. Appl Soft Comput 106610. https:\/\/doi.org\/10.1016\/j.asoc.2020.106610","DOI":"10.1016\/j.asoc.2020.106610"},{"issue":"8","key":"2002_CR16","doi-asserted-by":"publisher","first-page":"2615","DOI":"10.1109\/TMI.2020.2995965","volume":"39","author":"W Xinggang","year":"2020","unstructured":"Xinggang W, Xianbo D, Qing F et al (2020) A weakly-supervised framework for COVID-19 classification and lesion localization from chest CT. IEEE Trans Med Imaging 39(8):2615\u20132625. https:\/\/doi.org\/10.1109\/TMI.2020.2995965","journal-title":"IEEE Trans Med Imaging"},{"issue":"8","key":"2002_CR17","doi-asserted-by":"publisher","first-page":"2595","DOI":"10.1109\/TMI.2020.2995508","volume":"39","author":"X Ouyang","year":"2020","unstructured":"Ouyang X, Jiayu H, Liming X et al (2020) Dual-sampling attention network for diagnosis of COVID-19 from community acquired pneumonia. IEEE Trans Med Imaging 39(8):2595\u20132605. https:\/\/doi.org\/10.1109\/TMI.2020.2995508","journal-title":"IEEE Trans Med Imaging"},{"key":"2002_CR18","doi-asserted-by":"publisher","unstructured":"Chen L, Bentley P, Mori K et al (n.d.) Self-supervised learning for medical image analysis using image context restoration.\u00a0Med Image Anal 58:101539. https:\/\/doi.org\/10.1016\/j.media.2019.101539","DOI":"10.1016\/j.media.2019.101539"},{"key":"2002_CR19","doi-asserted-by":"publisher","first-page":"112957","DOI":"10.1016\/j.eswa.2019.112957","volume":"143","author":"F Gao","year":"2020","unstructured":"Gao F, Yoon H, Wu T et al (2020) A feature transfer enabled multi-task deep learning model on medical imaging. Expert Syst Appl 143:112957. https:\/\/doi.org\/10.1016\/j.eswa.2019.112957","journal-title":"Expert Syst Appl"},{"key":"2002_CR20","doi-asserted-by":"publisher","unstructured":"Kim M, Yan C, Yang D et al (2020) Chapter Eight-Deep learning in biomedical image analysis. In Biomedical Information Technology (Second Edition), Feng DD (ed) Academic Press, Cambridge, pp 239\u2013263. https:\/\/doi.org\/10.1016\/B978-0-12-816034-3.00008-0","DOI":"10.1016\/B978-0-12-816034-3.00008-0"},{"key":"2002_CR21","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1016\/j.neunet.2020.01.018","volume":"124","author":"D-X Zhou","year":"2020","unstructured":"Zhou D-X (2020) Theory of deep convolutional neural networks: Downsampling. Neural Netw 124:319\u2013327. https:\/\/doi.org\/10.1016\/j.neunet.2020.01.018","journal-title":"Neural Netw"},{"issue":"1","key":"2002_CR22","doi-asserted-by":"publisher","first-page":"6268","DOI":"10.1038\/s41598-019-42557-4","volume":"9","author":"F Pasa","year":"2019","unstructured":"Pasa F, Golkov V, Pfeiffer F et al (2019) Efficient deep network architectures for fast chest X-ray tuberculosis screening and visualization. Sci Rep 9(1):6268. https:\/\/doi.org\/10.1038\/s41598-019-42557-4","journal-title":"Sci Rep"},{"key":"2002_CR23","doi-asserted-by":"publisher","unstructured":"Miki Y, Muramatsu C, Hayashi T et al (n.d.) Classification of teeth in cone-beam CT using deep convolutional neural network. Comput Biol Med 80:24\u201329. https:\/\/doi.org\/10.1016\/j.compbiomed.2016.11.003","DOI":"10.1016\/j.compbiomed.2016.11.003"},{"key":"2002_CR24","unstructured":"Zhao JY, Zhang YC, He XH et al (2020) COVID-CT-Dataset: a CT scan dataset about COVID-19. ArXiv: abs\/2003.13865"},{"key":"2002_CR25","doi-asserted-by":"publisher","unstructured":"Song Y, Zheng S, Li L et al. Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. medRxiv. https:\/\/doi.org\/10.1101\/2020.02.23.20026930","DOI":"10.1101\/2020.02.23.20026930"},{"key":"2002_CR26","doi-asserted-by":"publisher","unstructured":"Wang S, Kang B, Ma J et al. A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). medRxiv. 2020.\u00a0https:\/\/doi.org\/10.1101\/2020.02.14.20023028","DOI":"10.1101\/2020.02.14.20023028"},{"key":"2002_CR27","doi-asserted-by":"publisher","unstructured":"Butt C, Gill J, Chun D et al (2020) Deep learning system to screen coronavirus disease 2019 pneumonia. Appl Intell. https:\/\/doi.org\/10.1007\/s10489-020-01714-3","DOI":"10.1007\/s10489-020-01714-3"},{"key":"2002_CR28","doi-asserted-by":"publisher","unstructured":"Khan AL, Junaid LS (2020) CoroNet: MB A Deep Neural Network for Detection and Diagnosis of Covid-19 from Chest X-ray Images. Comput Methods Programs Biomed 196(11):105581. https:\/\/doi.org\/10.1016\/j.cmpb.2020.105581","DOI":"10.1016\/j.cmpb.2020.105581"},{"key":"2002_CR29","doi-asserted-by":"publisher","unstructured":"Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B et al (2020) Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology :200905. https:\/\/doi.org\/10.1148\/radiol.2020200905","DOI":"10.1148\/radiol.2020200905"},{"key":"2002_CR30","unstructured":"Hanin B (2018) Which neural net architectures give rise to exploding and vanishing gradients? Neural information processing systems, pp 582\u2013591. ArVix: 1801.03744"},{"key":"2002_CR31","doi-asserted-by":"publisher","unstructured":"Vincent P, Larochelle H, Lajoie I et al (2010) Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res :3371\u20133408. https:\/\/doi.org\/10.5555\/1756006.1953039","DOI":"10.5555\/1756006.1953039"},{"key":"2002_CR32","doi-asserted-by":"publisher","unstructured":"Majumdar A, Aditay T (2017) Asymmetric stacked autoencoder. 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, Anchorage, pp 911\u2013918. https:\/\/doi.org\/10.1109\/IJCNN.2017.7965949","DOI":"10.1109\/IJCNN.2017.7965949"},{"issue":"6","key":"2002_CR33","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390. https:\/\/doi.org\/10.1145\/3065386","journal-title":"Commun ACM"},{"issue":"2","key":"2002_CR34","doi-asserted-by":"publisher","first-page":"1716","DOI":"10.1016\/j.amc.2005.09.016","volume":"175","author":"O K\u00f6ksoy","year":"2006","unstructured":"K\u00f6ksoy O (2006) Multiresponse robust design: Mean square loss (MSE) criterion. Appl Math Comput 175(2):1716\u20131729. https:\/\/doi.org\/10.1016\/j.amc.2005.09.016","journal-title":"Appl Math Comput"},{"key":"2002_CR35","unstructured":"Kingma D, Ba J (2015) Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015). arXiv:1412.6980"},{"key":"2002_CR36","unstructured":"Xu B, Ruitong H, Mu L (2016) Revise saturated activation functions. ICLR. arXiv:1602.05980"},{"key":"2002_CR37","doi-asserted-by":"publisher","unstructured":"Bottou L (2012) Stochastic gradient descent tricks. Neural Networks: Tricks of the Trade, pp 421\u2013436. https:\/\/doi.org\/10.1007\/978-3-642-35289-8_25","DOI":"10.1007\/978-3-642-35289-8_25"},{"key":"2002_CR38","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1038\/s41583-020-0277-3","volume":"21","author":"TP Lillicrap","year":"2020","unstructured":"Lillicrap TP, Santoro A, Marris L et al (2020) Backpropagation and the brain. Nat Rev Neurosci 21:335\u2013346. https:\/\/doi.org\/10.1038\/s41583-020-0277-3","journal-title":"Nat Rev Neurosci"},{"key":"2002_CR39","doi-asserted-by":"publisher","unstructured":"Saxe AM, Koh PW, Chen ZH et al (2011) On random weights and unsupervised feature learning. ICML 2(3). https:\/\/doi.org\/10.5555\/3104482.3104619","DOI":"10.5555\/3104482.3104619"},{"issue":"4","key":"2002_CR40","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","volume":"45","author":"M Sokolova","year":"2009","unstructured":"Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manag 45(4):427\u2013437. https:\/\/doi.org\/10.1016\/j.ipm.2009.03.002","journal-title":"Inf Process Manag"},{"key":"2002_CR41","doi-asserted-by":"publisher","unstructured":"Aggarwal A, Lohia P, Nagar S, Dey K, Saha D (2019) Black box fairness testing of machine learning models. Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering - ESEC\/FSE 2019. https:\/\/doi.org\/10.1145\/3338906.3338937","DOI":"10.1145\/3338906.3338937"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-02002-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-020-02002-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-02002-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,4]],"date-time":"2021-05-04T04:16:33Z","timestamp":1620101793000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-020-02002-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,9]]},"references-count":41,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,5]]}},"alternative-id":["2002"],"URL":"https:\/\/doi.org\/10.1007\/s10489-020-02002-w","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,9]]},"assertion":[{"value":"2 June 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 September 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 October 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 November 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}