Abstract
The severe damage caused by COVID-19 has become a reality, and there is no longer a way to save humanity from this epidemic except diagnose and prevention, especially with emergence delay and lack of vaccine recognized by the World Health Organization. Without therapeutic treatment or explicit restorative immunizations for COVID-19, it is fundamental to diagnose the disease at an early stage and quickly seclude patients contaminated with the virus. This study aims at estimating the damage via consistency of chest imaging, which is not always feasible or possible. Here, an application is proposed to solve the problem via a WEB Predictor ‘COVIDz” and a program exploiting deep learning, so as emergency care will be able to systematically bring chest X-ray images and predict the percentage of the absence or presence of COVID-19. The proposed approach (custom VGG model) and our WEB site “COVIDz” objective validation of the suggested solution obtained the best classification efficiency of 99.64%, F-score of 99.2%, precision of 99.28%, MCC of 99.28%, recall of 99.28%, and a specificity value of 100%.
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Acknowledgments
This work is supported by the General Directorate for Scientific Research and Technological Development (DGRSDT), Higher Education Ministry of Algeria, Algiers, Algeria. The authors would also like to thank Professor Abdelhakim Dinar from St. Peter’s Neurology, Albany, New York 12204, and also STIC Laboratory, Faculty of Technology, University of Tlemcen, Algeria.
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Oukebdane, M.A. et al. (2021). COVIDz: Deep Learning for Coronavirus Disease Detection. In: Kautish, S., Peng, SL., Obaid, A.J. (eds) Computational Intelligence Techniques for Combating COVID-19. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-68936-0_17
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