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

  1. Ozturk T., et al. “Automated detection of COVID-19 cases using deep neural networks with X-ray images”. Computers in Biology and Medicine (2020): ‏
  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).
  4. Butt C., et al. “Deep learning system to screen coronavirus disease 2019 pneumonia”. Applied Intelligence (2020).
  5. Ozcan T. “A Deep Learning Framework for Coronavirus Disease (COVID-19) Detection in X-Ray Images”. (2020).
  6. Trent McConghy., et al. “When does Hospital Capacity Get Overwhelmed in USA? Germany? A model of beds needed and available for Coronavirus patients”. trent.st. (2020).
  7. Song Y., et al. “Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images”. Published online February, 25, 2020-02 (2020).‏
  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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