Show simple item record

dc.contributor.authorHussain, B. Zahid
dc.contributor.authorAndleeb, Ifrah
dc.contributor.authorAnsari, Mohammad Samar
dc.contributor.authorJoshi, Amit Mahesh
dc.contributor.authorKanwal, Nadia
dc.date.accessioned2022-10-03T07:33:06Z
dc.date.available2022-10-03T07:33:06Z
dc.date.issued2022-09-08
dc.identifierhttps://chesterrep.openrepository.com/bitstream/handle/10034/627209/Zahid_Ifrah_GAN-2.pdf?sequence=1
dc.identifier.citationHussain, B. Z., Andleeb, I., Ansari, M. S., Joshi, A. M., & Kanwal, N. (2022). Wasserstein GAN based chest X-Ray dataset augmentation for deep learning models: COVID-19 detection use-case. 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 2058-2061. 10.1109/EMBC48229.2022.9871519.en_US
dc.identifier.doi10.1109/EMBC48229.2022.9871519
dc.identifier.urihttp://hdl.handle.net/10034/627209
dc.description“© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”en_US
dc.description.abstractThe novel coronavirus infection (COVID-19) is still continuing to be a concern for the entire globe. Since early detection of COVID-19 is of particular importance, there have been multiple research efforts to supplement the current standard RT-PCR tests. Several deep learning models, with varying effectiveness, using Chest X-Ray images for such diagnosis have also been proposed. While some of the models are quite promising, there still remains a dearth of training data for such deep learning models. The present paper attempts to provide a viable solution to the problem of data deficiency in COVID-19 CXR images. We show that the use of a Wasserstein Generative Adversarial Network (WGAN) could lead to an effective and lightweight solution. It is demonstrated that the WGAN generated images are at par with the original images using inference tests on an already proposed COVID-19 detection model.en_US
dc.publisherIEEEen_US
dc.relation.urlhttps://ieeexplore.ieee.org/document/9871519en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.subjectCovid 19en_US
dc.subjectChest X-Rayen_US
dc.titleWasserstein GAN based Chest X-Ray Dataset Augmentation for Deep Learning Models: COVID-19 Detection Use-Caseen_US
dc.typeConference Contributionen_US
dc.contributor.departmentAligarh Muslim University; University of Chester; Malaviya National Institute of Technology Jaipur; Keele Universityen_US
or.grant.openaccessYesen_US
rioxxterms.funderUnfundeden_US
rioxxterms.identifier.projectUnfundeden_US
rioxxterms.versionAMen_US
rioxxterms.versionofrecord10.1109/EMBC48229.2022.9871519en_US
dcterms.dateAccepted2022-04-01
rioxxterms.publicationdate2022-09-08
dc.date.deposited2022-10-03en_US


Files in this item

Thumbnail
Name:
Zahid_Ifrah_GAN-2.pdf
Size:
578.7Kb
Format:
PDF
Request:
Conference proceeding

This item appears in the following Collection(s)

Show simple item record

https://creativecommons.org/licenses/by-nc-nd/4.0/
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0/