Novel Coronavirus Disease 2019 (COVID-19) is a new pandemic that appeared at the end of March 2019 in Wuhan city, China, which affected millions worldwide. COVID-19 is caused by the novel severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) epidemic. Also, several viral epidemics have been listed in the last two decades, like the middle east respiratory syndrome coronavirus (MERSCoV) and the severe acute respiratory syndrome coronavirus 1 (SARSCoV-1), which cause MERS, and SARS diseases, respectively. Detection of these viral epidemics is a difficult issue because of their genetic similarity. In this paper, an effective automated system was developed to classify these viral epidemics using their complete genomic sequences via the genomic image processing techniques to facilitate the diagnosis and increase the detection accuracy in a short time. Results achieved an overall accuracy of 100% using two classifiers: SVM and KNN. However, the KNN classifier shows a privilege over the SVM in the execution time performance.

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The coronavirus disease 2019 (COVID-19) pandemic has been spreading quickly, threatening the public health system. Consequently, positive COVID-19 cases must be rapidly detected and treated. Automatic detection systems are essential for controlling the COVID-19 pandemic. Molecular techniques and medical imaging scans are among the most effective approaches for detecting COVID-19. Although these approaches are crucial for controlling the COVID-19 pandemic, they have certain limitations. This study proposes an effective hybrid approach based on genomic image processing (GIP) techniques to rapidly detect COVID-19 while avoiding the limitations of traditional detection techniques, using whole and partial genome sequences of human coronavirus (HCoV) diseases. In this work, the GIP techniques convert the genome sequences of HCoVs into genomic grayscale images using a genomic image mapping technique known as the frequency chaos game representation. Then, the pre-trained convolution neural network, AlexNet, is used to extract deep features from these images using the last convolution (conv5) and second fully-connected (fc7) layers. The most significant features were obtained by removing the redundant ones using the ReliefF and least absolute shrinkage and selection operator (LASSO) algorithms. These features are then passed to two classifiers: decision trees and k-nearest neighbors (KNN). Results showed that extracting deep features from the fc7 layer, selecting the most significant features using the LASSO algorithm, and executing the classification process using the KNN classifier is the best hybrid approach. The proposed hybrid deep learning approach detected COVID-19, among other HCoV diseases, with 99.71% accuracy, 99.78% specificity, and 99.62% sensitivity.

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