LAUSR

BACKGROUND Due to the COVID-19 pandemic and the imminent collapse of healthcare systems following the excessive consumption of financial, hospital, and medicinal resources, the WHO changed the alert level on the COVID-19 pandemic from high to very high. Meanwhile, the world began to favor less expensive and more precise COVID-19 detection methods. OBJECTIVE Machine vision-based COVID-19 detection methods especially Deep learning as a diagnostic technique in the early stages of the disease have found great importance during the pandemic. This study aimed to design a highly efficient CAD system for COVID-19 by using a NASNet-based algorithm. METHODS A state-of-the-art pre-trained CNN network for image feature extraction, called NASNet, was adopted to identify patients with COVID-19 in the first stages of the disease. A local dataset, comprising 10153 CT-scan images of 190 patients with COVID-19 and 59 with Non Covid-19, was used. RESULTS After fitting on the training dataset, hyper-parameter tuning and finally topological alterations of the classifier block, the proposed NASNet-based model was evaluated on the test dataset and yielded remarkable results. The proposed model's performance achieved a detection sensitivity, specificity, and accuracy of 0.999, 0.986, and 0.996, respectively. CONCLUSIONS The proposed model achieved acceptable results in the categorization of two data classes. Therefore, a CAD system was designed based on this model for COVID-19 detection using multiple lung CT scans. The system managed to differentiate all the COVID-19 cases from non-COVID-19 ones without any error in the application phase. Overall, the proposed deep learning-based CAD system can greatly aid radiologists in the detection of COVID-19 in its early stages. During the COVID-19 pandemic, the use of CAD system as a screening tool accelerates the process of disease detection and prevents the loss of healthcare resources. CLINICALTRIAL