Acta Scientific Otolaryngology (ASOL) (ISSN: 2582-5550)

Opinion Volume 4 Issue 11

Predicting the Severity of Patients with Coronavirus Using Neuronal Networks

Fatmah Altarrab*

Faculty Member in ENT Department, Faculty of Medicine, Damascus University, Syria

*Corresponding Author: Fatmah Altarrab, Faculty Member in ENT Department, Faculty of Medicine, Damascus University, Syria.

Received: September 07, 2022; Published: October 13, 2022

Although more than two years have passed since the beginning of the spread of the Coronavirus, there are no indications that the pandemic is about to leave, despite the massive vaccination campaigns and measures against this epidemic, as well as the availability of tests to detect infection early.

References

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  2. Narin A., et al. “Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks”. arXiv preprint arXiv (2020): 10849.‏
  3. Kwon M., et al. “Multi-label classification of single and clustered cervical cells using deep convolutional networks”. California State University, Los Angeles (2018).
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  8. Singh D., et al. “Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks”. European Journal of Clinical Microbiology and Infectious Diseases (2020): 1-11.‏
  9. Pourhomayoun M and Shakibi M. “Predicting mortality risk in patients with COVID-19 using artificial intelligence to help medical decision-making”. medRxiv (2020).‏
  10. Al-Tarrab F. “Predicting the severity of patients with coronavirus using neural networks”. Damascus University Journal of Engineering Sciences4 (2021).
  11. Al-Tarrab F., et al. “Improving the prediction of the severity of the condition of patients infected with the Corona virus”. Damascus University Journal of Engineering Sciences2 (2022).

Citation

Citation: Fatmah Altarrab. “Predicting the Severity of Patients with Coronavirus Using Neuronal Networks".Acta Scientific Otolaryngology 4.11 (2022): 18-19.

Copyright

Copyright: © 2022 Fatmah Altarrab. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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Impact Factor0.871

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