Covid-19 is a highly infectious disease that spreads extremely fast and is transmitted through indirect or direct contact. The scientists have categorized the Covid-19 cases into five different types: severe, critical, asymptomatic, moderate, and mild. Up to May 2021 more than 133.2 million peoples have been infected and almost 2.9 million people have lost their lives from Covid-19. To diagnose Covid-19, practitioners use RT-PCR tests that suffer from many False Positive (FP) and False Negative (FN) results while they take a long time. One solution to this is the conduction of a greater number of tests simultaneously to improve the True Positive (TP) ratio. However, CT-scan and X-ray images can also be used for early detection of Covid-19 related pneumonia. By the use of modern deep learning techniques, accuracy of more than 95% can be achieved. We used eight CNN (CovNet)-based deep learning models, namely ResNet 152 v2, InceptionResNet v2, Xception, Inception v3, ResNet 50, NASNetLarge, DenseNet 201, and VGG 16 for both X-rays and CT-scans to diagnose pneumonia. The achieved comparative results show that the proposed models are able to differentiate the Covid-19 positive cases.

The novel coronavirus (COVID-2019) pandemic has caused a catastrophic effect on health and global economy. Early screening and diagnosis of COVID-19 pneumonia are the critical steps to stop the further spread of the virus. The most common standard for confirming the virus relies on RT-PCR tests. This method generates false-negative results if there is limited viral load. Recent radiological findings suggest that the distinct distribution of ground-glass opacities (GGOs), which are found on certain parts of lungs, can determine the status of the infection among patients. As a complement to RT-PCR, Computed tomography (CT) can be used for diagnosing COVID-19. In this study, the authors have described a Mask R-CNN (region-based convolution neural network) approach for the detection of the ground glass opacities (GGOs) in chest CT images of COVID-19 infected persons. The proposed approach provides an accuracy of 98.25% during instance segmentation. Therefore, the authors believe this proposed method will aid health professionals to fasten the screening and validation of the initial assessment towards COVID-19 patients.

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