  We present an automatic COVID1-19 diagnosis framework from lung CT images.
The focus is on signal processing and classification on small datasets with
efforts putting into exploring data preparation and augmentation to improve the
generalization capability of the 2D CNN classification models. We propose a
unique and effective data augmentation method using multiple Hounsfield Unit
(HU) normalization windows. In addition, the original slice image is cropped to
exclude background, and a filter is applied to filter out closed-lung images.
For the classification network, we choose to use 2D Densenet and Xception with
the feature pyramid network (FPN). To further improve the classification
accuracy, an ensemble of multiple CNN models and HU windows is used. On the
training/validation dataset, we achieve a patient classification accuracy of
93.39%.
