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Chest X-ray is the first imaging technique that plays an important role inthe diagnosis of COVID-19 disease. Due to the high availability of large-scaleannotated image datasets, great success has been achieved using convolutionalneural networks (CNNs) for image recognition and classification. However, dueto the limited availability of annotated medical images, the classification ofmedical images remains the biggest challenge in medical diagnosis. Thanks totransfer learning, an effective mechanism that can provide a promising solutionby transferring knowledge from generic object recognition tasks todomain-specific tasks. In this paper, we validate and adapt our previouslydeveloped CNN, called Decompose, Transfer, and Compose (DeTraC), for theclassification of COVID-19 chest X-ray images. DeTraC can deal with anyirregularities in the image dataset by investigating its class boundaries usinga class decomposition mechanism. The experimental results showed the capabilityof DeTraC in the detection of COVID-19 cases from a comprehensive image datasetcollected 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 of93.36%) was achieved by DeTraC in the detection of COVID-19 X-ray images fromnormal, and severe acute respiratory syndrome cases.

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network