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In International journal of imaging systems and technology

Among Coronavirus, as with many other viruses, receptor interactions are an essential determinant of species specificity, virulence, and pathogenesis. The pathogenesis of the COVID-19 depends on the virus's ability to attach to and enter into a suitable human host cell. This paper presents a cockroach optimized deep neural network to detect COVID-19 and differentiate between COVID-19 and influenza types A, B, and C. The deep network architecture is inspired using a cockroach optimization algorithm to optimize the deep neural network hyper-parameters. COVID-19 sequences are obtained from repository 2019 Novel Coronavirus Resource, and influenza A, B, and C sub-dataset are obtained from other repositories. Five hundred ninety-four unique genomes sequences are used in the training and testing process with 99% overall accuracy for the classification model.

El-Dosuky Mohamed A, Soliman Mona, Hassanien Aboul Ella


COVID‐19, SARS‐CoV‐2, cockroach swarm optimization, convolutional neural networks, coronavirus, deep learning, influenza