  We introduce a comprehensive screening platform for the COVID-19 (a.k.a.,
SARS-CoV-2) pneumonia. The proposed AI-based system works on chest x-ray (CXR)
images to predict whether a patient is infected with the COVID-19 disease.
Although the recent international joint effort on making the availability of
all sorts of open data, the public collection of CXR images is still relatively
small for reliably training a deep neural network (DNN) to carry out COVID-19
prediction. To better address such inefficiency, we design a cascaded learning
strategy to improve both the sensitivity and the specificity of the resulting
DNN classification model. Our approach leverages a large CXR image dataset of
non-COVID-19 pneumonia to generalize the original well-trained classification
model via a cascaded learning scheme. The resulting screening system is shown
to achieve good classification performance on the expanded dataset, including
those newly added COVID-19 CXR images.
