Expanding new COVID-19 data with conditional generative adversarial networks
Joint Authors
Majid, Hanin
Ali, Khawlah Husayn
Source
The Iraqi Journal of Electrical and Electronic Engineering
Issue
Vol. 18, Issue 1 (30 Jun. 2022), pp.103-110, 8 p.
Publisher
University of Basrah College of Engineering
Publication Date
2022-06-30
Country of Publication
Iraq
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
COVID-19 is an infectious viral disease that mostly affects the lungs.
that quickly spreads across the world.
early detection of the virus boosts the chances of patients recovering quickly worldwide.
many radiographic techniques are used to diagnose an infected person such as X-rays, deep learning technology based on a large amount of chest x-ray images is used to diagnose COVID-19 disease.
because of the scarcity of available COVID-19 X-rays image, the limited COVID-19 Datasets are insufficient for efficient deep learning detection models.
another problem with a limited dataset is that training models suffer from over-fitting, and the predictions are not generalizable to address these problems.
in this paper, we developed conditional generative adversarial networks (CGAN) to produce synthetic images close to real images for the COVID-19 case and traditional augmentation that was used to expand the limited dataset then used to train by Customized deep detection model.
the customized deep learning model was able to obtain excellent detection accuracy of 97% accurate with only ten epochs.
the proposed augmentation outperforms other augmentation techniques.
the augmented dataset includes 6988 high-quality and resolution COVID-19 X-rays images.
at the same time, the original COVID-19 X-rays images are only 587.
American Psychological Association (APA)
Majid, Hanin& Ali, Khawlah Husayn. 2022. Expanding new COVID-19 data with conditional generative adversarial networks. The Iraqi Journal of Electrical and Electronic Engineering،Vol. 18, no. 1, pp.103-110.
https://search.emarefa.net/detail/BIM-1380214
Modern Language Association (MLA)
Majid, Hanin& Ali, Khawlah Husayn. Expanding new COVID-19 data with conditional generative adversarial networks. The Iraqi Journal of Electrical and Electronic Engineering Vol. 18, no. 1 (Jun. 2022), pp.103-110.
https://search.emarefa.net/detail/BIM-1380214
American Medical Association (AMA)
Majid, Hanin& Ali, Khawlah Husayn. Expanding new COVID-19 data with conditional generative adversarial networks. The Iraqi Journal of Electrical and Electronic Engineering. 2022. Vol. 18, no. 1, pp.103-110.
https://search.emarefa.net/detail/BIM-1380214
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 109-110
Record ID
BIM-1380214