Highlights
We proposed a novel framework, CHP-Net, to differentiate and localize COVID-19 from community acquired pneumonia.
We used excessive data augmentation to extend the available dataset and optimize the CHP-Net generalization capability.
Comparing to other ConvNet, CHP-Net works much more efficiently to extract feature information on chest X-Ray.
All metrics, including categorical loss, accuracy, precision, recall and F1-score, proved CHP-Net fits good for the task.
CHP-Net are better than the previous methods tested in detecting COVID-19 and exceeding to radiologist.