  The current COVID-19 pandemic has motivated the researchers to use artificial
intelligence techniques for a potential alternative to reverse
transcription-polymerase chain reaction (RT-PCR) due to the limited scale of
testing. The chest X-ray (CXR) is one of the alternatives to achieve fast
diagnosis but the unavailability of large-scale annotated data makes the
clinical implementation of machine learning-based COVID detection difficult.
Another issue is the usage of ImageNet pre-trained networks which does not
extract reliable feature representations from medical images. In this paper, we
propose the use of hierarchical convolutional network (HCN) architecture to
naturally augment the data along with diversified features. The HCN uses the
first convolution layer from COVIDNet followed by the convolutional layers from
well-known pre-trained networks to extract the features. The use of the
convolution layer from COVIDNet ensures the extraction of representations
relevant to the CXR modality. We also propose the use of ECOC for encoding
multiclass problems to binary classification for improving the recognition
performance. Experimental results show that HCN architecture is capable of
achieving better results in comparison to the existing studies. The proposed
method can accurately triage potential COVID-19 patients through CXR images for
sharing the testing load and increasing the testing capacity.
