  The novel corona virus (Covid-19) has introduced significant challenges due
to its rapid spreading nature through respiratory transmission. As a result,
there is a huge demand for Artificial Intelligence (AI) based quick disease
diagnosis methods as an alternative to high demand tests such as Polymerase
Chain Reaction (PCR). Chest X-ray (CXR) Image analysis is such cost-effective
radiography technique due to resource availability and quick screening. But, a
sufficient and systematic data collection that is required by complex deep
leaning (DL) models is more difficult and hence there are recent efforts that
utilize transfer learning to address this issue. Still these transfer learnt
models suffer from lack of generalization and increased bias to the training
dataset resulting poor performance for unseen data. Limited correlation of the
transferred features from the pre-trained model to a specific medical imaging
domain like X-ray and overfitting on fewer data can be reasons for this
circumstance. In this work, we propose a novel Graph Convolution Neural Network
(GCN) that is capable of identifying bio-markers of Covid-19 pneumonia from CXR
images and meta information about patients. The proposed method exploits
important relational knowledge between data instances and their features using
graph representation and applies convolution to learn the graph data which is
not possible with conventional convolution on Euclidean domain. The results of
extensive experiments of proposed model on binary (Covid vs normal) and three
class (Covid, normal, other pneumonia) classification problems outperform
different benchmark transfer learnt models, hence overcoming the aforementioned
drawbacks.
