nanoll extt
Please use this identifier to cite or link to this item: http://lrcdrs.bennett.edu.in:80/handle/123456789/1182
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSingh, Dilbag-
dc.date.accessioned2023-04-04T19:03:49Z-
dc.date.available2023-04-04T19:03:49Z-
dc.date.issued2020-
dc.identifier.issn1868-5137-
dc.identifier.urihttps://doi.org/10.1007/s12652-020-02669-6-
dc.identifier.urihttp://lrcdrs.bennett.edu.in:80/handle/123456789/1182-
dc.description.abstractThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes novel coronavirus disease (COVID-19) outbreak in more than 200 countries around the world. The early diagnosis of infected patients is needed to discontinue this outbreak. The diagnosis of coronavirus infection from radiography images is the fastest method. In this paper, two different ensemble deep transfer learning models have been designed for COVID-19 diagnosis utilizing the chest X-rays. Both models have utilized pre-trained models for better performance. They are able to differentiate COVID-19, viral pneumonia, and bacterial pneumonia. Both models have been developed to improve the generalization capability of the classifier for binary and multi-class problems. The proposed models have been tested on two well-known datasets. Experimental results reveal that the proposed framework outperforms the existing techniques in terms of sensitivity, specificity, and accuracy. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.en_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.subjectChest X-rayen_US
dc.subjectCOVID-19en_US
dc.subjectEnsemble modelsen_US
dc.subjectSARS-CoV-2en_US
dc.subjectTransfer learningen_US
dc.titleRapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic imagesen_US
dc.typeArticleen_US
dc.indexedscen_US
Appears in Collections:Journal Articles_SCSET

Files in This Item:
File Description SizeFormat 
481.pdf
  Restricted Access
1.84 MBAdobe PDFView/Open Request a copy

Contact admin for Full-Text

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.