{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T21:31:23Z","timestamp":1785879083203,"version":"3.56.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T00:00:00Z","timestamp":1666137600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T00:00:00Z","timestamp":1666137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12071475"],"award-info":[{"award-number":["12071475"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11671010"],"award-info":[{"award-number":["11671010"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["4172035"],"award-info":[{"award-number":["4172035"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005236","name":"Chinese Universities Scientific Fund","doi-asserted-by":"publisher","award":["2022TC083"],"award-info":[{"award-number":["2022TC083"]}],"id":[{"id":"10.13039\/501100005236","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Precision Agriculture Application Project","award":["JZNYYY001"],"award-info":[{"award-number":["JZNYYY001"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1007\/s13042-022-01676-7","type":"journal-article","created":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T14:03:48Z","timestamp":1666188228000},"page":"973-987","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["CovidViT: a novel neural network with self-attention mechanism to detect Covid-19 through X-ray images"],"prefix":"10.1007","volume":"14","author":[{"given":"Hang","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liyang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7577-4420","authenticated-orcid":false,"given":"Yitian","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuhua","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,19]]},"reference":[{"issue":"10223","key":"1676_CR1","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 et al (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395(10223):497\u2013506","journal-title":"Lancet"},{"issue":"4","key":"1676_CR2","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1007\/s12098-020-03263-6","volume":"87","author":"T Singhal","year":"2020","unstructured":"Singhal T (2020) A review of coronavirus disease-2019 (COVID-19). Indian J Pediatr 87(4):281\u2013286","journal-title":"Indian J Pediatr"},{"key":"1676_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2020.108961","volume":"126","author":"C Long","year":"2020","unstructured":"Long C, Xu H, Shen Q, Zhang X, Fan B, Wang C, Zeng B, Li Z, Li X, Li H (2020) Diagnosis of the coronavirus disease (COVID-19): rRT-PCR or CT? Eur J Radiol 126:108961","journal-title":"Eur J Radiol"},{"issue":"2","key":"1676_CR4","doi-asserted-by":"publisher","first-page":"E15","DOI":"10.1148\/radiol.2020200490","volume":"296","author":"ZY Zu","year":"2020","unstructured":"Zu ZY, Jiang MD, Xu PP, Chen W, Ni QQ, Lu GM, Zhang LJ (2020) Coronavirus disease 2019 (COVID-19): a perspective from China. Radiology 296(2):E15\u2013E25","journal-title":"Radiology"},{"key":"1676_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cmpb.2018.04.005","volume":"161","author":"O Faust","year":"2018","unstructured":"Faust O, Hagiwara Y, Hong TJ, Lih OS, Acharya UR (2018) Deep learning for healthcare applications based on physiological signals: a review. Comput Methods Progr Biomed 161:1\u201313","journal-title":"Comput Methods Progr Biomed"},{"key":"1676_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103792","volume":"121","author":"T Ozturk","year":"2020","unstructured":"Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Acharya UR (2020) Automated detection of COVID-19 cases using deep neural networks with X-ray images. Compute Biol Med 121:103792","journal-title":"Compute Biol Med"},{"issue":"2","key":"1676_CR7","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1007\/s13246-020-00865-4","volume":"43","author":"ID Apostolopoulos","year":"2020","unstructured":"Apostolopoulos ID, Mpesiana TA (2020) COVID-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med 43(2):635\u2013640","journal-title":"Phys Eng Sci Med"},{"issue":"2","key":"1676_CR8","first-page":"1301","volume":"66","author":"RA Al-Falluji","year":"2021","unstructured":"Al-Falluji RA (2021) Automatic detection of COVID-19 using chest X-ray images and modified ResNet18-based convolution neural networks. Comput Mater Contin 66(2):1301\u20131313","journal-title":"Comput Mater Contin"},{"key":"1676_CR9","unstructured":"Vaswani A, Shazeer N, Parmar N et al (2017) Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17). Curran Associates Inc., Red Hook, NY, USA, 6000\u20136010"},{"key":"1676_CR10","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D et al (2021) An image is worth 16x16 words: transformers for image recognition at scale. In: 2021 International conference on learning representations (ICLR), pp 1\u201314"},{"key":"1676_CR11","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: 2015 International conference on learning representations (ICLR)"},{"key":"1676_CR12","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep Residual Learning for Image Recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"1676_CR13","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton G (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097\u20131105","journal-title":"Adv Neural Inf Process Syst"},{"key":"1676_CR14","doi-asserted-by":"publisher","first-page":"132665","DOI":"10.1109\/ACCESS.2020.3010287","volume":"8","author":"M Chowdhury","year":"2020","unstructured":"Chowdhury M, Rahman T, Khandakar A et al (2020) Can AI help in screening viral and COVID-19 pneumonia? IEEE Access 8:132665\u2013132676","journal-title":"IEEE Access"},{"key":"1676_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104319","volume":"132","author":"T Rahman","year":"2021","unstructured":"Rahman T, Khandakar A, Qiblawey Y et al (2021) Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images. Comput Biol Med 132:104319","journal-title":"Comput Biol Med"},{"key":"1676_CR16","doi-asserted-by":"publisher","unstructured":"Wu B, Xu C, Dai X, Wan A, Zhang P, Yan Z, Tomizuka M, Gonzalez J, Keutzer K, Vajda P (2020) Visual transformers: token-based image representation and processing for computer vision. CoRR. https:\/\/doi.org\/10.48550\/arxiv.2006.03677","DOI":"10.48550\/arxiv.2006.03677"},{"key":"1676_CR17","doi-asserted-by":"publisher","unstructured":"Deng J, Dong W, Socher R, Li L, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition (CVPR), pp 248\u2013255. https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"9","key":"1676_CR18","doi-asserted-by":"publisher","first-page":"2080","DOI":"10.1080\/03610926.2019.1568485","volume":"49","author":"G Zeng","year":"2020","unstructured":"Zeng G (2020) On the confusion matrix in credit scoring and its analytical properties. Commun Stat Theory Methods 49(9):2080\u20132093","journal-title":"Commun Stat Theory Methods"},{"issue":"2","key":"1676_CR19","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/S0720-048X(97)00157-5","volume":"27","author":"A Erkel","year":"1998","unstructured":"Erkel A, Pattynama P (1998) Receiver operating characteristic (ROC) analysis: basic principles and applications in radiology. Eur J Radiol 27(2):88\u201394","journal-title":"Eur J Radiol"},{"key":"1676_CR20","doi-asserted-by":"publisher","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: Visual explanations from deep networks via gradient-based localization. In: 2017 Proceedings of the IEEE international conference on computer vision (ICCV), pp 618\u2013626. https:\/\/doi.org\/10.1109\/ICCV.2017.74","DOI":"10.1109\/ICCV.2017.74"},{"key":"1676_CR21","unstructured":"Gildenblat J and Contributors (2021) PyTorch library for CAM methods. GitHub. https:\/\/github.com\/jacobgil\/pytorch-grad-cam. Accessed 20 Nov 2021"},{"issue":"1","key":"1676_CR22","doi-asserted-by":"publisher","first-page":"E167","DOI":"10.1148\/radiol.2020203511","volume":"299","author":"RM Wehbe","year":"2021","unstructured":"Wehbe RM, Sheng J, Dutta S, Chai S et al (2021) DeepCOVID-XR: an artificial intelligence algorithm to detect COVID-19 on chest radiographs trained and tested on a large US clinical data set. Radiology 299(1):E167\u2013E176","journal-title":"Radiology"},{"issue":"1","key":"1676_CR23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-56847-4","volume":"10","author":"L Wang","year":"2020","unstructured":"Wang L, Lin Z, 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 10(1):1\u201312","journal-title":"Sci Rep"},{"issue":"4","key":"1676_CR24","first-page":"643","volume":"5","author":"PK Sethy","year":"2020","unstructured":"Sethy PK, Santi K, Behera et al (2020) Detection of coronavirus disease (COVID-19) based on deep features and Support Vector Machine. Int J Math Eng Manag Sci 5(4):643\u2013651","journal-title":"Int J Math Eng Manag Sci"},{"key":"1676_CR25","doi-asserted-by":"publisher","unstructured":"Dev K et al (2021) Triage of potential COVID-19 patients from chest X-ray images using hierarchical convolutional networks. Neural Comput Appl.\u00a0https:\/\/doi.org\/10.1007\/s00521-020-05641-9","DOI":"10.1007\/s00521-020-05641-9"},{"key":"1676_CR26","doi-asserted-by":"publisher","unstructured":"Loey M, Manogaran G, Khalifa NEM (2020) A deep transfer learning model with classical data augmentation and CGAN to detect COVID-19 from chest CT radiography digital images. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-020-05437-x","DOI":"10.1007\/s00521-020-05437-x"},{"key":"1676_CR27","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1016\/j.ins.2022.01.062","volume":"592","author":"Md Kawsher Mahbub","year":"2022","unstructured":"Kawsher Mahbub Md, Biswas M, Gaur L et al (2022) Deep features to detect pulmonary abnormalities in chest X-rays due to infectious diseaseX: Covid-19, pneumonia, and tuberculosis. Inf Sci 592:389\u2013401","journal-title":"Inf Sci"},{"key":"1676_CR28","doi-asserted-by":"publisher","first-page":"2777","DOI":"10.1007\/s10489-020-01943-6","volume":"51","author":"H Mukherjee","year":"2021","unstructured":"Mukherjee H, Ghosh et al (2021) Deep neural network to detect COVID-19: one architecture for both CT Scans and Chest X-rays. Appl Intell 51:2777\u20132789","journal-title":"Appl Intell"},{"issue":"71","key":"1676_CR29","first-page":"1","volume":"45","author":"KC Santosh","year":"2021","unstructured":"Santosh KC, Ghosh S (2021) Covid-19 imaging tools: how big data is big? J Med Syst 45(71):1\u20138","journal-title":"J Med Syst"},{"issue":"5","key":"1676_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-020-01562-1","volume":"44","author":"KC Santosh","year":"2020","unstructured":"Santosh KC (2020) AI-driven tools for coronavirus outbreak: need of active learning and cross-population train\/test models on multitudinal\/multimodal data. J Med Syst 44(5):1\u20135","journal-title":"J Med Syst"},{"key":"1676_CR31","doi-asserted-by":"crossref","unstructured":"Santosh KC, Ghosh et al (2022) Deep learning for Covid-19 screening using chest X-rays in 2020: a systematic review. Int J Pattern Recognit Artif Intell 36(5):2252010","DOI":"10.1142\/S0218001422520103"},{"key":"1676_CR32","unstructured":"Kingma Diederik P, Adam JB (2015) A method for stochastic optimization. In: 2015 International conference on learning representations (ICLR)"},{"key":"1676_CR33","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1007\/s13246-020-00888-x","volume":"43","author":"D Das","year":"2020","unstructured":"Das D, Santosh KC, Pal U (2020) Truncated inception net: COVID-19 outbreak screening using chest X-rays. Phys Eng Sci Med 43:915\u2013925","journal-title":"Phys Eng Sci Med"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01676-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-022-01676-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01676-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T03:51:22Z","timestamp":1677037882000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-022-01676-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,19]]},"references-count":33,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["1676"],"URL":"https:\/\/doi.org\/10.1007\/s13042-022-01676-7","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,19]]},"assertion":[{"value":"28 December 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 October 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 declared that they have no conflicts of interest to this work.","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"}]}}