Congress:
ECR25
Poster Number:
C-11189
Type:
Poster: EPOS Radiologist (scientific)
Authorblock:
M. M. Masoomi1, L. A. Al Kandari2, H. R. E. Elziat2, A. Mohammad2, T. Shaker3; 1Portsmouth/UK, 2KUWAIT/KW, 3Cairo/EG
Disclosures:
Michael Michael Masoomi:
Nothing to disclose
Latifa Abdullah Al Kandari:
Nothing to disclose
Haytham Ramzy Elsayed Elziat:
Nothing to disclose
Amr Mohammad:
Nothing to disclose
Taha Shaker:
Nothing to disclose
Keywords:
Artificial Intelligence, Emergency, Lung, Conventional radiography, Digital radiography, Computer Applications-Detection, diagnosis, Screening, Chronic obstructive airways disease, Education and training, Infection
REFERENCES
- Smith, J. R., Mason, D. E., & Cummings, J. (2020). The role of chest X-ray in diagnosing pneumonia and COVID-19. Radiology Today, 41(5), 23-27.
- Lee, C. H., Ho, M. L., & Wang, H. P. (2019). Chest X-ray as a diagnostic tool for pneumonia. Journal of Clinical Imaging Science, 9(3), 12-17.
- World Health Organization (WHO). (2020). Coronavirus disease (COVID-19) advice for the public. https://www.who.int
- Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swatter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.
- LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.