Abstract
The novel disease COVID-19 is due to infection by a new virus named SARS CoV-2 which is very different from other corona-viruses such as MERS or SARS and has a higher infection rate. The infection rate could be kept low if COVID-19 cases were detected and isolated sooner. Even though many vaccines were made available, SARS CoV-2 has multiple variants such as Alpha, Beta, Gamma, and Delta which are more contagious and resistant to vaccines. Moreover, only 10% of the worldwide population is fully vaccinated up to this date due to vaccines shortage and anti-vaxxers campaigns. Thus, according to these reasons, more measures need to be considered to contain this pandemic. This study proposed to perform COVID-19 cases detection, based on X-ray images, using machine learning and deep learning algorithms. Some hybrid approaches are investigated such as performing feature extraction using deep learning algorithms and classification using machine learning methods. The best model on four class data sets that achieved 0.9109 accuracy was a hybrid model where features were extracted using VGG16 deep neural network and the classification was done by VotingClassifier.
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Abdoul-Razak, A.B., Mikram, M., Rhanoui, M., Ghouzali, S. (2022). Hybrid Machine and Deep Transfer Learning Based Classification Models for Covid 19 and Pneumonia Diagnosis Using X-ray Images. In: Maleh, Y., Alazab, M., Gherabi, N., Tawalbeh, L., Abd El-Latif, A.A. (eds) Advances in Information, Communication and Cybersecurity. ICI2C 2021. Lecture Notes in Networks and Systems, vol 357. Springer, Cham. https://doi.org/10.1007/978-3-030-91738-8_37
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DOI: https://doi.org/10.1007/978-3-030-91738-8_37
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