Abstract
COVID-19 has spread rapidly all over the world and has infected more than 200 countries and regions. Early screening of suspected infected patients is essential for preventing and combating COVID-19. Computed Tomography (CT) is a fast and efficient tool which can quickly provide chest scan results. To reduce the burden on doctors of reading CTs, in this article, a high precision diagnosis algorithm of COVID-19 from chest CTs is designed for intelligent diagnosis. A semi-supervised learning approach is developed to solve the problem when only small amount of labelled data is available. While following the MixMatch rules to conduct sophisticated data augmentation, we introduce a model training technique to reduce the risk of model over-fitting. At the same time, a new data enhancement method is proposed to modify the regularization term in MixMatch. To further enhance the generalization of the model, a convolutional neural network based on an attention mechanism is then developed that enables to extract multi-scale features on CT scans. The proposed algorithm is evaluated on an independent CT dataset of the chest from COVID-19 and achieves the area under the receiver operating characteristic curve (AUC) value of 0.932, accuracy of 90.1%, sensitivity of 91.4%, specificity of 88.9%, and F1-score of 89.9%. The results show that the proposed algorithm can accurately diagnose whether a chest CT belongs to a positive or negative indication of COVID-19, and can help doctors to diagnose rapidly in the early stages of a COVID-19 outbreak.
【저자키워드】 COVID-19, deep learning, semi-supervised learning, Computed tomography, Attention mechanisms, 【초록키워드】 Diagnosis, risk, Spread, sensitivity, specificity, Chest CT, COVID-19 outbreak, Accuracy, Chest, Algorithm, dataset, CT scans, characteristic, early stage, mechanism, diagnose, AUC, Precision, help, positive, chest scan, while, regions, approach, country, feature, independent, combating, ENhance, evaluated, infected patient, reduce, modify, 【제목키워드】 chest CT scan, approach,