Wasserstein GAN based Chest X-Ray Dataset Augmentation for Deep Learning Models: COVID-19 Detection Use-Case
dc.contributor.author | Hussain, B. Zahid | |
dc.contributor.author | Andleeb, Ifrah | |
dc.contributor.author | Ansari, Mohammad Samar | |
dc.contributor.author | Joshi, Amit Mahesh | |
dc.contributor.author | Kanwal, Nadia | |
dc.date.accessioned | 2022-10-03T07:33:06Z | |
dc.date.available | 2022-10-03T07:33:06Z | |
dc.date.issued | 2022-09-08 | |
dc.identifier | https://chesterrep.openrepository.com/bitstream/handle/10034/627209/Zahid_Ifrah_GAN-2.pdf?sequence=1 | |
dc.identifier.citation | Hussain, 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.doi | 10.1109/EMBC48229.2022.9871519 | |
dc.identifier.uri | http://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.abstract | The 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.publisher | IEEE | en_US |
dc.relation.url | https://ieeexplore.ieee.org/document/9871519 | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | en_US |
dc.subject | Covid 19 | en_US |
dc.subject | Chest X-Ray | en_US |
dc.title | Wasserstein GAN based Chest X-Ray Dataset Augmentation for Deep Learning Models: COVID-19 Detection Use-Case | en_US |
dc.type | Conference Contribution | en_US |
dc.contributor.department | Aligarh Muslim University; University of Chester; Malaviya National Institute of Technology Jaipur; Keele University | en_US |
or.grant.openaccess | Yes | en_US |
rioxxterms.funder | Unfunded | en_US |
rioxxterms.identifier.project | Unfunded | en_US |
rioxxterms.version | AM | en_US |
rioxxterms.versionofrecord | 10.1109/EMBC48229.2022.9871519 | en_US |
dcterms.dateAccepted | 2022-04-01 | |
rioxxterms.publicationdate | 2022-09-08 | |
dc.date.deposited | 2022-10-03 | en_US |