Novel multi-site graph convolutional network with supervision mechanism for COVID-19 diagnosis from X-ray radiographs

The novel Coronavirus disease 2019 (COVID-2019) has become a global pandemic and affected almost all aspects of our daily life. The total number of positive COVID-2019 cases has exponentially increased in the last few months due to the easy transmissibility of the virus. It can be detected using the nucleic acid test or the antibodies blood test which are not always available and take several hours to get the results. Therefore, researchers proposed computer-aided diagnosis systems using the state-of-the-art artificial intelligence techniques to learn imaging biomarkers from chest computed tomography and X-ray radiographs to effectively diagnose COVID-19. However, previous methods either adopted transfer learning from a pre-trained model on natural images or were trained on limited datasets. Either cases may lead to accuracy deficiency or overfitting. In addition, feature space suffers from noise and outliers when collecting X-ray images from multiple datasets. In this paper, we overcome the previous limitations by firstly collecting a large-scale X-ray dataset from multiple resources. Our dataset includes 11,312 images collected from 10 different data repositories. To alleviate the effect of the noise, we suppress it in the feature space of our new dataset. Secondly, we introduce a supervision mechanism and combine it with the VGG-16 network to consider the differences between the COVID-19 and healthy cases in the feature space. Thirdly, we propose a multi-site (center) COVID-19 graph convolutional network (GCN) that exploits dataset information, the status of training samples, and initial scores to effectively classify the disease status. Extensive experiments using different convolutional neural network-based methods with and without the supervision mechanism and different classifiers are performed. Results demonstrate the effectiveness of the proposed supervision mechanism in all models and superior performance with the proposed GCN.