Background Owing to the COVID-19 pandemic and the imminent collapse of health care systems following the exhaustion of financial, hospital, and medicinal resources, the World Health Organization changed the alert level of the COVID-19 pandemic from high to very high. Meanwhile, more cost-effective and precise COVID-19 detection methods are being preferred worldwide. Objective Machine vision–based COVID-19 detection methods, especially deep learning as a diagnostic method in the early stages of the pandemic, have been assigned great importance during the pandemic. This study aimed to design a highly efficient computer-aided detection (CAD) system for COVID-19 by using a neural search architecture network (NASNet)–based algorithm. Methods NASNet, a state-of-the-art pretrained convolutional neural network for image feature extraction, was adopted to identify patients with COVID-19 in their early stages of the disease. A local data set, comprising 10,153 computed tomography scans of 190 patients with and 59 without COVID-19 was used. Results After fitting on the training data set, hyperparameter tuning, and topological alterations of the classifier block, the proposed NASNet-based model was evaluated on the test data set 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 2 data classes. Therefore, a CAD system was designed on the basis of this model for COVID-19 detection using multiple lung computed tomography scans. The system differentiated all 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 help radiologists detect COVID-19 in its early stages. During the COVID-19 pandemic, the use of a CAD system as a screening tool would accelerate disease detection and prevent the loss of health care resources.
【저자키워드】 COVID-19, coronavirus, pandemic, deep learning, artificial intelligence, machine learning, classification, Convolutional neural network, Model, computer-aided detection, computed tomography scan, machine vision, 【초록키워드】 Health care, COVID-19 pandemic, hospital, lung, Local, Computed tomography, specificity, Health, Accuracy, Algorithm, Convolutional neural network, Patient, Exhaustion, disease detection, Health care system, Care, early stage, Diagnostic method, CAD, Detection sensitivity, health care systems, data set, Health Organization, World Health Organization, Radiologists, Classifier, COVID-19 case, alteration, help, early stages, machine, categorization, training data, radiologist, Computed tomography scans, resources, objective, Prevent, Result, identify, was used, detect, the disease, evaluated, adopted, changed, assigned, accelerate, patients with COVID-19, 【제목키워드】 detection, Multiple, System, deep, Neural,