  As the rapid spread of coronavirus disease (COVID-19) worldwide, chest X-ray
radiography has also been used to detect COVID-19 infected pneumonia and assess
its severity or monitor its prognosis in the hospitals due to its low cost, low
radiation dose, and wide accessibility. However, how to more accurately and
efficiently detect COVID-19 infected pneumonia and distinguish it from other
community-acquired pneumonia remains a challenge. In order to address this
challenge, we in this study develop and test a new computer-aided diagnosis
(CAD) scheme. It includes several image pre-processing algorithms to remove
diaphragms, normalize image contrast-to-noise ratio, and generate three input
images, then links to a transfer learning based convolutional neural network (a
VGG16 based CNN model) to classify chest X-ray images into three classes of
COVID-19 infected pneumonia, other community-acquired pneumonia and normal
(non-pneumonia) cases. To this purpose, a publicly available dataset of 8,474
chest X-ray images is used, which includes 415 confirmed COVID-19 infected
pneumonia, 5,179 community-acquired pneumonia, and 2,880 non-pneumonia cases.
The dataset is divided into two subsets with 90% and 10% of images in each
subset to train and test the CNN-based CAD scheme. The testing results achieve
94.0% of overall accuracy in classifying three classes and 98.6% accuracy in
detecting Covid-19 infected cases. Thus, the study demonstrates the feasibility
of developing a CAD scheme of chest X-ray images and providing radiologists
useful decision-making supporting tools in detecting and diagnosis of COVID-19
infected pneumonia.
