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The novel corona virus (Covid-19) has introduced significant challenges dueto its rapid spreading nature through respiratory transmission. As a result,there is a huge demand for Artificial Intelligence (AI) based quick diseasediagnosis methods as an alternative to high demand tests such as PolymeraseChain Reaction (PCR). Chest X-ray (CXR) Image analysis is such cost-effectiveradiography technique due to resource availability and quick screening. But, asufficient and systematic data collection that is required by complex deepleaning (DL) models is more difficult and hence there are recent efforts thatutilize transfer learning to address this issue. Still these transfer learntmodels suffer from lack of generalization and increased bias to the trainingdataset resulting poor performance for unseen data. Limited correlation of thetransferred features from the pre-trained model to a specific medical imagingdomain like X-ray and overfitting on fewer data can be reasons for thiscircumstance. In this work, we propose a novel Graph Convolution Neural Network(GCN) that is capable of identifying bio-markers of Covid-19 pneumonia from CXRimages and meta information about patients. The proposed method exploitsimportant relational knowledge between data instances and their features usinggraph representation and applies convolution to learn the graph data which isnot possible with conventional convolution on Euclidean domain. The results ofextensive experiments of proposed model on binary (Covid vs normal) and threeclass (Covid, normal, other pneumonia) classification problems outperformdifferent benchmark transfer learnt models, hence overcoming the aforementioneddrawbacks.

Covid-19 Detection from Chest X-ray and Patient Metadata using Graph Convolutional Neural Networks