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Top1. Introduction
Coronavirus Disease 2019 (COVID-19) is a novel (new) virus that first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is contagious respiratory illnesses that is caused by infection with a new coronavirus (called SARS-CoV-2), which affects different people in different ways. The centers for disease control and prevention (CDC) are closely monitoring the spread of cases caused by this disease. As of the best of our knowledge while we write this article and according to the World Health Organization (WHO), more than 60 million confirmed cases glob- ally, and more than 1 million deaths. The current tests are mostly based on reverse- transcription polymerase chain reaction (RT-PCR), which looks for bits of the virus’s genetic material in the patient’s blood or sputum sample. The testing may not be sensitive enough to detect COVID-19 in people with the infection. In addition, during the peak time of COVID-19 outbreak, RT-PCR test kits were in shortage (Yang et al., 2020). To overcome of RT-PCR limitation, many imaging techniques can be widely used to examine patient with COVID-19 such as Chest x-ray (CRX) and CT scan are (Rubin et al, 2020; Cohen et al., 2020). In this study, the assessment or examination processes to identify COVID-19 is the chest CT, which is recommended to be used as the primary screening or diagnostic method. Chest CTs are fast and relatively easy to perform and undergo. They are also demonstrated more sensitive to COVID-19 infection and better performance to detect the positive cases than CRX (Benmalek et al., 2021; Borakati et al., 2020). Therefore, the CAD systems are recommended to detect the earliest signs of ground-glass nodules in thoracic CT that are caused by this disease, which may not be detected by the medical professionals at the early times. In Fig. 1, image A shows that COVID-19 causes multiple peripheral ground-glass opacities in lung that did not spare the subpleural regions, while image B shows progressive produced pulmonary opacities after 3 days (J. Lei et al., 2020).
The main motivation of this research is to assist accelerating the diagnostic process and help stopping this widespread pandemic. Therefore, we introduce the CAD system that applies the advanced deep learning-based radiology image analysis methods as a complementary to the com- pressive learning (CL), which is based on different sensing matrices weighted strategy. This CAD system could outperform many state-of-the-art methods.