  Chest X-ray is the first imaging technique that plays an important role in
the diagnosis of COVID-19 disease. Due to the high availability of large-scale
annotated image datasets, great success has been achieved using convolutional
neural networks (CNNs) for image recognition and classification. However, due
to the limited availability of annotated medical images, the classification of
medical images remains the biggest challenge in medical diagnosis. Thanks to
transfer learning, an effective mechanism that can provide a promising solution
by transferring knowledge from generic object recognition tasks to
domain-specific tasks. In this paper, we validate and adapt our previously
developed CNN, called Decompose, Transfer, and Compose (DeTraC), for the
classification of COVID-19 chest X-ray images. DeTraC can deal with any
irregularities in the image dataset by investigating its class boundaries using
a class decomposition mechanism. The experimental results showed the capability
of DeTraC in the detection of COVID-19 cases from a comprehensive image dataset
collected from several hospitals around the world. High accuracy of 95.12%
(with a sensitivity of 97.91%, a specificity of 91.87%, and a precision of
93.36%) was achieved by DeTraC in the detection of COVID-19 X-ray images from
normal, and severe acute respiratory syndrome cases.
