Covid-19 Detection from Chest X-Ray using ACGAN and RESNET

Authors

  • Arun Raj S Department of Computer Science and Engineering, Younus College of Engineering and Technology, Kollam, Kerala, India
  • Anand. SB Department of Computer Science and Engineering, Younus College of Engineering and Technology, Kollam, Kerala, India
  • Fathima B Department of Computer Science and Engineering, Younus College of Engineering and Technology, Kollam, Kerala, India
  • Ponnu Raj R. Department of Computer Science and Engineering, Younus College of Engineering and Technology, Kollam, Kerala, India

DOI:

https://doi.org/10.26438/ijcse/v9i6.4953

Keywords:

COVID-19, ACGAN, CXR

Abstract

COVID-19 is a viral infection brought about by Coronavirus 2 (SARS-CoV-2). The spread of COVID-19 appears to have a hindering impact on the worldwide Economy and wellbeing. A positive chest X-beam of contaminated patients is a urgent advance in the fight against COVID-19. This has prompted the presentation of an assortment of profound learning frameworks and studies have shown that the exactness of COVID-19 patient recognition using chest X- beams is unequivocally idealistic. Profound learning organizations like convolutional neural organizations (CNNs) need a significant measure of preparing information. In this task, we present a technique to create engineered chest X-beam (CXR) pictures by fostering an Auxiliary Classifier Generative Adversarial Network (ACGAN) based Model called Covid GAN. Also, the proposed framework shows that the engineered pictures created from Covid GAN can be used to improve the exhibition of CNN based design called Resnet.

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Published

2021-06-30
CITATION
DOI: 10.26438/ijcse/v9i6.4953
Published: 2021-06-30

How to Cite

[1]
A. R. S, A. SB, F. B, and P. Raj R., “Covid-19 Detection from Chest X-Ray using ACGAN and RESNET”, Int. J. Comp. Sci. Eng., vol. 9, no. 6, pp. 49–53, Jun. 2021.

Issue

Vol. 9 No. 6 (2021): IJCSE June Edition

Section

Research Article

License

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