{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T06:38:44Z","timestamp":1757313524679,"version":"3.37.3"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T00:00:00Z","timestamp":1688428800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T00:00:00Z","timestamp":1688428800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s00500-023-08874-7","type":"journal-article","created":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T13:05:00Z","timestamp":1688994300000},"page":"14241-14251","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A new method for chest X-ray images categorization using transfer learning and CovidNet_2020 employing convolution neural network"],"prefix":"10.1007","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2451-0411","authenticated-orcid":false,"given":"P. S.","family":"Raghavendran","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4665-9068","authenticated-orcid":false,"given":"S.","family":"Ragul","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1674-8200","authenticated-orcid":false,"given":"R.","family":"Asokan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5962-2961","authenticated-orcid":false,"given":"Ashok Kumar","family":"Loganathan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9156-2054","authenticated-orcid":false,"given":"Suresh","family":"Muthusamy","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5158-4857","authenticated-orcid":false,"given":"Om Prava","family":"Mishra","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7309-3138","authenticated-orcid":false,"given":"Ponarun","family":"Ramamoorthi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7929-1194","authenticated-orcid":false,"given":"Suma Christal Mary","family":"Sundararajan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,4]]},"reference":[{"key":"8874_CR1","doi-asserted-by":"publisher","first-page":"E32","DOI":"10.1148\/radiol.2020200642","volume":"296","author":"Ai","year":"2020","unstructured":"Ai et al (2020) Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology 296:E32\u2013E40","journal-title":"Radiology"},{"key":"8874_CR2","doi-asserted-by":"crossref","unstructured":"Amin-Naji M, Mahdavinataj H, Aghagolzadeh A (2019) Alzheimer's disease diagnosis from structural MRI using Siamese convolutional neural network. In: 4th international conference on pattern recognition and image analysis (IPRIA), Tehran, Iran, 2019, pp 75\u201379","DOI":"10.1109\/PRIA.2019.8786031"},{"key":"8874_CR3","doi-asserted-by":"crossref","unstructured":"Apostolopoulos D, Bessiana T (2020) Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. arXiv:2003.11617","DOI":"10.1007\/s13246-020-00865-4"},{"key":"8874_CR4","doi-asserted-by":"publisher","first-page":"12501","DOI":"10.1007\/s00500-020-04687-0","volume":"24","author":"OA Arqub","year":"2020","unstructured":"Arqub OA, Al-Smadi M (2020) Fuzzy conformable fractional differential equations: novel extended approach and new numerical solutions. Soft Comput 24:12501\u201312522. https:\/\/doi.org\/10.1007\/s00500-020-04687-0","journal-title":"Soft Comput"},{"key":"8874_CR5","doi-asserted-by":"publisher","first-page":"3283","DOI":"10.1007\/s00500-015-1707-4","volume":"20","author":"OA Arqub","year":"2016","unstructured":"Arqub OA, Al-Smadi M, Momani S et al (2016) Numerical solutions of fuzzy differential equations using reproducing kernel Hilbert space method. Soft Comput 20:3283\u20133302. https:\/\/doi.org\/10.1007\/s00500-015-1707-4","journal-title":"Soft Comput"},{"key":"8874_CR6","doi-asserted-by":"publisher","first-page":"7191","DOI":"10.1007\/s00500-016-2262-3","volume":"21","author":"OA Arqub","year":"2017","unstructured":"Arqub OA, Al-Smadi M, Momani S et al (2017) Application of reproducing kernel algorithm for solving second-order, two-point fuzzy boundary value problems. Soft Comput 21:7191\u20137206. https:\/\/doi.org\/10.1007\/s00500-016-2262-3","journal-title":"Soft Comput"},{"key":"8874_CR7","first-page":"20","volume":"54","author":"S Attia","year":"2016","unstructured":"Attia S (2016) Enhancement of chest X-ray images for diagnosis purposes. Adv Phys Theories Appl 54:20\u201323","journal-title":"Adv Phys Theories Appl"},{"key":"8874_CR8","doi-asserted-by":"crossref","unstructured":"Bullock J, Luccioni A, Pham KH, Lam CS, Luengo-Oroz M (2020) Mapping the landscape of artificial intelligence applications against COVID-19. arXiv:2003.11336","DOI":"10.1613\/jair.1.12162"},{"key":"8874_CR9","doi-asserted-by":"crossref","unstructured":"Chen G, Yang C, Xie S (2006) Gradient-based structural similarity for image quality assessment. In: 2006 international conference on image processing, Atlanta, GA, pp 2929\u20132932","DOI":"10.1109\/ICIP.2006.313132"},{"key":"8874_CR10","doi-asserted-by":"crossref","unstructured":"Chowdhury MEH, Rahman T, Khandakar A et al (2020) Can AI help in screening Viral and COVID-19 pneumonia? arXiv:2003.13145 [cs.CV]. https:\/\/www.kaggle.com\/tawsifurrahman\/covid19-radiography-database","DOI":"10.1109\/ACCESS.2020.3010287"},{"key":"8874_CR11","unstructured":"Cohen JP, Morrison P, Dao L (2020) COVID-19 image data collection. arXiv:2003.11597. https:\/\/github.com\/ieee8023\/covid-chestxray-dataset"},{"key":"8874_CR12","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.media.2016.06.032","volume":"33","author":"M de Bruijne","year":"2016","unstructured":"de Bruijne M (2016) Machine learning approaches in medical image analysis: from detection to diagnosis. Med Image Anal 33:94\u201397","journal-title":"Med Image Anal"},{"key":"8874_CR13","doi-asserted-by":"publisher","first-page":"E115","DOI":"10.1148\/radiol.2020200432","volume":"296","author":"Fang","year":"2020","unstructured":"Fang et al (2020) Sensitivity of chest CT for covid-19: comparison to RT-PCR. Radiology 296:E115\u2013E117","journal-title":"Radiology"},{"key":"8874_CR14","unstructured":"Islam MT et al (2017) Abnormality detection and localization in chest X-rays using deep convolutional neural networks. arXiv:1705.09850v3 [cs.CV]"},{"key":"8874_CR15","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1148\/radiol.2020200241","volume":"295","author":"JP Kanne","year":"2020","unstructured":"Kanne JP (2020) Chest CT findings in 2019 novel coronavirus (2019-nCoV) infections from Wuhan, China: key points for the radiologist. Radiology 295:16\u201317","journal-title":"Radiology"},{"key":"8874_CR16","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1016\/j.cjph.2022.10.002","volume":"80","author":"B Maayah","year":"2022","unstructured":"Maayah B, Arqub OA, Alnabulsi S, Alsulami H (2022) Numerical solutions and geometric attractors of a fractional model of the cancer-immune based on the Atangana\u2013Baleanu\u2013Caputo derivative and the reproducing kernel scheme. Chin J Phys 80:463\u2013483","journal-title":"Chin J Phys"},{"key":"8874_CR17","doi-asserted-by":"crossref","unstructured":"Narin A, Kaya C, Pamuk Z (2020) Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. arXiv:2003.10849","DOI":"10.1007\/s10044-021-00984-y"},{"key":"8874_CR18","unstructured":"Pinho E, Costa C (2018) Feature learning with adversarial networks for concept detection in medical images: UA.PT Bioinformatics at ImageCLEF 2018"},{"key":"8874_CR19","unstructured":"Raghu M, Zhang C, Kleinberg J, Bengio S (2019) Transfusion: understanding transfer learning for medical imaging. arXiv:1902.07208 [cs.CV]"},{"issue":"2","key":"8874_CR20","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1109\/TMM.2007.911837","volume":"10","author":"MX Ribeiro","year":"2008","unstructured":"Ribeiro MX, Traina AJM, Traina C, Azevedo-Marques PM (2008) An association rule-based method to support medical image diagnosis with efficiency. IEEE Trans Multimed 10(2):277\u2013285","journal-title":"IEEE Trans Multimed"},{"key":"8874_CR22","doi-asserted-by":"crossref","unstructured":"Song Y, Zheng S, Li L, Zhang X, Zhang X, Huang Z et al (2020) Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. MedRxiv","DOI":"10.1109\/TCBB.2021.3065361"},{"key":"8874_CR23","doi-asserted-by":"crossref","unstructured":"Tang Y, Tang Y, Han M, Xiao J, Summers RM (2019) Abnormal chest X-ray identification with generative adversarial one-class classifier. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019), Venice, Italy, pp 1358\u20131361","DOI":"10.1109\/ISBI.2019.8759442"},{"key":"8874_CR24","first-page":"425","volume":"5","author":"KAG Udeshani","year":"2011","unstructured":"Udeshani KAG, Meegama G, Fernando TGI (2011) Statistical feature-based neural network approach for the detection of lung cancer in chest X-ray image. Int J Image Process (IJIP) 5:425\u2013434","journal-title":"Int J Image Process (IJIP)"},{"key":"8874_CR25","doi-asserted-by":"publisher","first-page":"564","DOI":"10.3390\/sym10110564","volume":"10","author":"T Vo","year":"2018","unstructured":"Vo T, Nguyen T, Le C (2018) Race recognition using deep convolutional neural networks. Symmetry 10:564. https:\/\/doi.org\/10.3390\/sym10110564","journal-title":"Symmetry"},{"key":"8874_CR26","doi-asserted-by":"crossref","unstructured":"Wang L, Lin ZQWong A (2020) COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. arXiv:2003.09871v3 [eess.IV]","DOI":"10.1038\/s41598-020-76550-z"},{"key":"8874_CR27","unstructured":"Zhang J, Xie Y, Li Y, Shen C, Xia Y (2020) COVID-19 screening on chest X-ray images using deep learning based anomaly detection. arXiv:2003.12338v1 [eess.IV]"},{"key":"8874_CR28","doi-asserted-by":"crossref","unstructured":"Zoph B, Vasudevan V, Shlens J, Le QV (2018) Learning transferable architectures for scalable image recognition. arXiv:1707.07012v4, 11 Apr 2018","DOI":"10.1109\/CVPR.2018.00907"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-023-08874-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-023-08874-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-023-08874-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T17:16:16Z","timestamp":1692897376000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-023-08874-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,4]]},"references-count":27,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["8874"],"URL":"https:\/\/doi.org\/10.1007\/s00500-023-08874-7","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"type":"print","value":"1432-7643"},{"type":"electronic","value":"1433-7479"}],"subject":[],"published":{"date-parts":[[2023,7,4]]},"assertion":[{"value":"17 June 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 July 2023","order":2,"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 no potential conflicts of interest with respect to the research, authorship, and\/or publication of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This material is the authors' own original work, which has not been previously published elsewhere. The paper is not currently being considered for publication elsewhere. The paper reflects the authors' own research and analysis in a truthful and complete manner.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"I have been informed of the risks and benefits involved, and all my questions have been answered to my satisfaction. Furthermore, I have been assured that any future questions I may have will also be answered by a member of the research team. I voluntarily agree to take part in this study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Individuals may consent to participate in a study, but object to having their data published in a journal article.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}}]}}