DETECTION OF COVID-19 WITH CHEST X-RAY
In order to speed up finding of causes of COVID-19 illness, this study developed novel diagnostic platform using profound convolutional neural network (CNN) helping radiologists diagnose COVID-19 pneumonia beside non-COVID-19 pneumonia in patient in Middle more Hospital. As the name suggests, crucial objective of our research is to produce a chest X-ray image classification program which could properly identify a scan's categorization as either "normal," "viral pneumonia," or "COVID-19." Using X-rays, we will train an image classifier to determine whether or not a person has COVID-19. In this data set, there are over 3000 chest X-ray pictures categorized in normal, viral, as well as COVID-19. A picture classifying system which properly identifies which of three categories Chest X-Ray scan corresponds with is purpose of this investigation.
W. Yang, A. Sirajuddin, X. Zhang, G. Liu, Z. Teng, S. Zhao, M. Lu, “The role of imaging in 2019 novel coronavirus pneumonia (COVID- 19),” European Radiology, vol. April 15, pp. 1- 9, 2020.
G. D. Rubin, C. J. Ryerson, L. B. Haramati, N. Sverzellati, J. P. Kanne, S. Raoof, N. W. Schluger, A. Volpi, J.-J. Yim, I. B. K. Martin, D. J. Anderson, C. Kong, T. Altes, A. Bush et al, “The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society,” Radiology, vol. Apr 7, 2020.
M. P. Cheng, J. Papenburg, M. Desjardins, S. Kanjila, C. Quach, M. Libman, S. Dittrich, C. P. Yansouni, “Diagnostic Testing for Severe Acute Respiratory Syndrome–Related Coronavirus-2: A Narrative Review,” Annals of Internal Medicine, vol. 13 Apr, 2020.
Jacobi, M. Chung, A. Bernheim, C. Eber, “Portable chest X-ray in coronavirus disease-19 (COVID-19): A pictorial review,” Clinical Imaging, vol.64,pp.35-42, 2020. Crossref, Medline.
World Health Organisation, situation reports on COVID-19 outbreak,” 15 April 2020. [Online]. Available: https://apps.who.int/iris/bitstream/handl e/10665/331763/SITREP_COVID- 19_WHOAFRO_20200415-eng.pdf.
D. J. Mollura, P. M. Lungren, Radiology in Global Health, Strategies, Implementation, and Applications, Springer, 2019. Crossref,
E. J. Hwang, J. G. Nam, W. H. Lim, S. J. Park, Y.S. Jeong, J. H. Kang, E. K. Hong, T. M. Kim, J. M. Goo, S. Park, K. H. Kim, C. M. Park, “Deep Learning for Chest Radiography Diagnosis in the Emergency Department,” Radiology, vol. 293, no. 3, 2019.
M. Annarumma, S. J. Withey, R. J. Bakewell, E. Pesce, V. Goh, G. Montana, “Automated Triaging of Adult Chest Radiographs with Deep Artificial Neural Networks,” Radiology, vol. 291, no. 1, 2019.
K. Murphy, S. S. Habib, S. M. A. Zaidi, S. Khowaja, A. Khan, J. Melendez, E. T. Scholten, F. Amad, S. Schalekamp, M. Verhagen, R. H. H. M. Philipsen, A. Meijers, B. v. Ginneken, “Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system,” Scientific Reports, vol. 10, 2020.
Z. Z. Qin, M. S. Sander, B. Rai, C. N. Titahong, S. Sudrungrot, S. N. Laah, L. M. Adhikari, E. J. Carter, L. Puri, A. J. Codlin, J. Creswell, “Using artificial intelligence to read chest radiographs for tuberculosis detection: A multi-site evaluation of the diagnostic accuracy of three deep learning systems,” Scientific Reports, vol. 9, 2019.
Copyright (c) 2022 IJRDO -Journal of Computer Science Engineering
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Author(s) and co-author(s) jointly and severally represent and warrant that the Article is original with the author(s) and does not infringe any copyright or violate any other right of any third parties, and that the Article has not been published elsewhere. Author(s) agree to the terms that the IJRDO Journal will have the full right to remove the published article on any misconduct found in the published article.