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In this paper, we consider the problem of Coronavirus disease (COVID-19) diagnosis from chest X-ray images in a multi-label classification scenario, where the ultimate goal is to distinguish among Healthy, non-COVID Pneumonia, and COVID-19 infection cases from the chest X-ray manifestations. Particularly, we establish the use of a rapid, non-invasive and cost-effective X-ray-based method as a key diagnosis and screening tool for COVID-19 at early and intermediate stages of the disease. To this end, we propose CoroNet, a deep learning framework that is built upon a two-stage learning methodology: 1) an AutoEncoder to extract the infected regions in the chest X-ray manifestation of COVID-19 and other Pneumonia-like diseases and 2) a deep convolutional neural network for the multi-label classification. We utilize this tailored deep architecture to extract the relevant features specific to each class to perform the task of automatic diagnosis and classification. The unsupervised part of the proposed framework helps with proper identification of the disease given the scarcity of quality datasets on COVID-19, and at the same time, facilitates exploiting the large X-ray datasets that are readily available for Healthy and non-COVID Pneumonia cases. Our numerical investigations demonstrate that the proposed framework outperforms the state-of-the-art methods for COVID-19 identification while employing approximately ten times fewer training parameters as compared to other existing methodologies. Furthermore, we make use of attribution maps, an explainable artificial intelligence tool, to interpret the diagnosis offered by the network. We have made the codes of our proposed CoroNet framework publicly available to the research community.
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