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The current COVID-19 pandemic has motivated the researchers to use artificialintelligence techniques for a potential alternative to reversetranscription-polymerase chain reaction (RT-PCR) due to the limited scale oftesting. The chest X-ray (CXR) is one of the alternatives to achieve fastdiagnosis but the unavailability of large-scale annotated data makes theclinical implementation of machine learning-based COVID detection difficult.Another issue is the usage of ImageNet pre-trained networks which does notextract reliable feature representations from medical images. In this paper, wepropose the use of hierarchical convolutional network (HCN) architecture tonaturally augment the data along with diversified features. The HCN uses thefirst convolution layer from COVIDNet followed by the convolutional layers fromwell-known pre-trained networks to extract the features. The use of theconvolution layer from COVIDNet ensures the extraction of representationsrelevant to the CXR modality. We also propose the use of ECOC for encodingmulticlass problems to binary classification for improving the recognitionperformance. Experimental results show that HCN architecture is capable ofachieving better results in comparison to the existing studies. The proposedmethod can accurately triage potential COVID-19 patients through CXR images forsharing the testing load and increasing the testing capacity.

Triage of Potential COVID-19 Patients from Chest X-ray Images using Hierarchical Convolutional Networks