  The Coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China,
in early December 2019 and now becoming a pandemic. When COVID-19 patients
undergo radiography examination, radiologists can observe the present of
radiographic abnormalities from their chest X-ray (CXR) images. In this study,
a deep convolutional neural network (CNN) model was proposed to aid
radiologists in diagnosing COVID-19 patients. First, this work conducted a
comparative study on the performance of modified VGG-16, ResNet-50 and
DenseNet-121 to classify CXR images into normal, COVID-19 and viral pneumonia.
Then, the impact of image augmentation on the classification results was
evaluated. The publicly available COVID-19 Radiography Database was used
throughout this study. After comparison, ResNet-50 achieved the highest
accuracy with 95.88%. Next, after training ResNet-50 with rotation,
translation, horizontal flip, intensity shift and zoom augmented dataset, the
accuracy dropped to 80.95%. Furthermore, an ablation study on the effect of
image augmentation on the classification results found that the combinations of
rotation and intensity shift augmentation methods obtained an accuracy higher
than baseline, which is 96.14%. Finally, ResNet-50 with rotation and intensity
shift augmentations performed the best and was proposed as the final
classification model in this work. These findings demonstrated that the
proposed classification model can provide a promising result for COVID-19
diagnosis.
