This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopted advanced deep network architectures and proposed a transfer learning strategy using custom-sized input tailored for each deep architecture to achieve the best performance. We conducted extensive sets of experiments on two CT image datasets, namely, the SARS-CoV-2 CT-scan and the COVID19-CT. The results show superior performances for our models compared with previous studies. Our best models achieved average accuracy, precision, sensitivity, specificity, and F1-score values of 99.4 % , 99.6 % , 99.8 % , 99.6 % , and 99.4 % on the SARS-CoV-2 dataset, and 92.9 % , 91.3 % , 93.7 % , 92.2 % , and 92.5 % on the COVID19-CT dataset, respectively. For better interpretability of the results, we applied visualization techniques to provide visual explanations for the models’ predictions. Feature visualizations of the learned features show well-separated clusters representing CT images of COVID-19 and non-COVID-19 cases. Moreover, the visualizations indicate that our models are not only capable of identifying COVID-19 cases but also provide accurate localization of the COVID-19-associated regions, as indicated by well-trained radiologists.
【저자키워드】 SARS-CoV-2, coronavirus, COVID-19 detection, explainable deep learning, feature visualization, 【초록키워드】 COVID-19, deep learning, sensitivity, specificity, Accuracy, Cluster, automated, dataset, experiment, CT-scan, diagnose, COVID-19 cases, Chest CT images, best, network architectures, Radiologists, Precision, Previous studies, COVID-19 case, average, transfer, datasets, non-COVID-19 cases, Chest CT image, regions, feature, indicated, conducted, applied, adopted, representing, the SARS-CoV-2, 【제목키워드】 COVID-19, detection, learning, deep,