Issue |
E3S Web Conf.
Volume 297, 2021
The 4th International Conference of Computer Science and Renewable Energies (ICCSRE'2021)
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Article Number | 01031 | |
Number of page(s) | 9 | |
DOI | https://doi.org/10.1051/e3sconf/202129701031 | |
Published online | 22 September 2021 |
COVID-19 Detection using Chest X-Ray
Department of Computer Engineering, Vivekanand Education Society’s Institute of Technology, Chembur, India
Department of Computer engineering, Vivekanand Education Society’s Institute of Technology, Chembur, India
Department of Computer Engineering, Vivekanand Education Society’s Institute of Technology, Chembur, India
Department of Computer Engineering, Vivekanand Education Society’s Institute of Technology, Chembur, India
Department of Computer Engineering, Vivekanand Education Society’s Institute of Technology, Chembur, India
2017.sagar.raheja@ves.ac.in
2017.jatin.chhabria@ves.ac.in
2017.gaurang.wadhwa@ves.ac.in
2017.samay.ahuja@ves.ac.in
anjali.yeole@ves.ac.in
Over the past few months, the exponential increase in COVID-19 cases has been overwhelming for many healthcare systems across the world. With 114 million cases globally as of 28th February 2021, with India itself having 11.1 million cases, it has challenged us with the testing, quarantine, and safety measures. Having limited testing kits, not all patients that have symptoms of respiratory illness can be tested using conventional techniques (RT-PCR). In this project, we propose the use of chest X-Ray to prioritize the selection of patients for further RT-PCR testing. It would also help in identifying patients with a high likelihood of COVID with a false negative RT-PCR who would wish to repeat testing. Further, we propose the utilization of recent AI techniques to detect the COVID-19 patients automatically using X-Ray images, particularly in settings where radiologists aren’t available, and help make the proposed testing technology scalable.
Key words: COVID-19 / healthcare systems / X-Ray / RT-PCR / patient / AI
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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