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

Arun Raj S.1 , Anand. S. B.2 , Fathima B.3 , Ponnu Raj R.4

Section:Research Paper, Product Type: Journal Paper
Volume-9 , Issue-6 , Page no. 49-53, Jun-2021

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v9i6.4953

Online published on Jun 30, 2021

Copyright © Arun Raj S., Anand. S. B.,Fathima B., Ponnu Raj R. . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Arun Raj S., Anand. S. B.,Fathima B., Ponnu Raj R., “Covid-19 Detection from Chest X-Ray using ACGAN and RESNET,” International Journal of Computer Sciences and Engineering, Vol.9, Issue.6, pp.49-53, 2021.

MLA Style Citation: Arun Raj S., Anand. S. B.,Fathima B., Ponnu Raj R. "Covid-19 Detection from Chest X-Ray using ACGAN and RESNET." International Journal of Computer Sciences and Engineering 9.6 (2021): 49-53.

APA Style Citation: Arun Raj S., Anand. S. B.,Fathima B., Ponnu Raj R., (2021). Covid-19 Detection from Chest X-Ray using ACGAN and RESNET. International Journal of Computer Sciences and Engineering, 9(6), 49-53.

BibTex Style Citation:
@article{S._2021,
author = {Arun Raj S., Anand. S. B.,Fathima B., Ponnu Raj R.},
title = {Covid-19 Detection from Chest X-Ray using ACGAN and RESNET},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {6 2021},
volume = {9},
Issue = {6},
month = {6},
year = {2021},
issn = {2347-2693},
pages = {49-53},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5347},
doi = {https://doi.org/10.26438/ijcse/v9i6.4953}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v9i6.4953}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5347
TI - Covid-19 Detection from Chest X-Ray using ACGAN and RESNET
T2 - International Journal of Computer Sciences and Engineering
AU - Arun Raj S., Anand. S. B.,Fathima B., Ponnu Raj R.
PY - 2021
DA - 2021/06/30
PB - IJCSE, Indore, INDIA
SP - 49-53
IS - 6
VL - 9
SN - 2347-2693
ER -

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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.

Key-Words / Index Term

COVID-19, ACGAN, CXR.

References

[1] Shervin Minaee, Rahele Kafieh, Milan Sonka, Shakib Yazdani, and Ghazaleh Jamalipour Soufi,” Deep-covid: Predicting covid-19 from chest x-ray images using deep transfer learning”, Medical image analysis, 65:101794, 2020.
[2] Parnian Afshar, Shahin Heidarian, Farnoosh Naderkhani, Anastasia Oikonomou, Konstantinos N Plataniotis, and Arash Mohammadi, “Covidcaps: A capsule network-based framework for identification of covid-19 cases from x-ray images”, Pattern Recognition Letters, 138:638–643, 2020.
[3] Linda Wang, Zhong Qiu Lin, and Alexander Wong,” Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images,” Scientific Reports, 10(1):1–12, 2020.
[4] Eduardo Luz, Pedro Lopes Silva, Rodrigo Silva, Ludmila Silva, Gladston Moreira, and David Menotti,” Towards an effective and efficient deep learning model for covid-19 patterns detection in x-ray images”, arXiv preprint arXiv:2004.05717, 2020.
[5] Enzo Tartaglione, Carlo Alberto Barbano, Claudio Berzovini, Marco Calandri, and Marco Grangetto,” Unveiling covid-19 from chest x-ray with deep learning: a hurdles race with small data”, International Journal of Environmental Research and Public Health, 17(18):6933, 2020.