The reverse transcriptase polymerase chain reaction (RT-PCR) is still the routinely used test for the diagnosis of SARS-CoV-2 (COVID-19). However, according to several reports, RT-PCR showed a low sensitivity and multiple tests may be required to rule out false negative results. Recently, chest computed tomography (CT) has been an efficient tool to diagnose COVID-19 as it is directly affecting the lungs. In this paper, we investigate the application of pre-trained models in diagnosing patients who are positive for COVID-19 and differentiating it from normal patients, who tested negative for coronavirus. The study aims to compare the generalization capabilities of deep learning models with two thoracic radiologists in diagnosing COVID-19 chest CT images. A dataset of 3000 images was obtained from the Near East Hospital, Cyprus, and used to train and to test the three employed pre-trained models. In a test set of 250 images used to evaluate the deep neural networks and the radiologists, it was found that deep networks (ResNet-18, ResNet-50, and DenseNet-201) can outperform the radiologists in terms of higher accuracy (97.8%), sensitivity (98.1%), specificity (97.3%), precision (98.4%), and F1-score (198.25%), in classifying COVID-19 images.
【초록키워드】 COVID-19, coronavirus, deep learning, RT-PCR, Chest computed tomography, sensitivity, specificity, Accuracy, Lungs, Patient, Cyprus, dataset, patients, diagnose, Thoracic, Chest CT images, Radiologists, Precision, diagnosing, positive, false negative results, transcriptase, radiologist, East, polymerase chain, tested, required, affecting, was obtained, diagnosis of SARS-CoV-2, used to evaluate, 【제목키워드】 COVID-19, Comparative, radiologist, deep, Neural,