  The newly identified Coronavirus pneumonia, subsequently termed COVID-19, is
highly transmittable and pathogenic with no clinically approved antiviral drug
or vaccine available for treatment. The most common symptoms of COVID-19 are
dry cough, sore throat, and fever. Symptoms can progress to a severe form of
pneumonia with critical complications, including septic shock, pulmonary edema,
acute respiratory distress syndrome and multi-organ failure. While medical
imaging is not currently recommended in Canada for primary diagnosis of
COVID-19, computer-aided diagnosis systems could assist in the early detection
of COVID-19 abnormalities and help to monitor the progression of the disease,
potentially reduce mortality rates. In this study, we compare popular deep
learning-based feature extraction frameworks for automatic COVID-19
classification. To obtain the most accurate feature, which is an essential
component of learning, MobileNet, DenseNet, Xception, ResNet, InceptionV3,
InceptionResNetV2, VGGNet, NASNet were chosen amongst a pool of deep
convolutional neural networks. The extracted features were then fed into
several machine learning classifiers to classify subjects as either a case of
COVID-19 or a control. This approach avoided task-specific data pre-processing
methods to support a better generalization ability for unseen data. The
performance of the proposed method was validated on a publicly available
COVID-19 dataset of chest X-ray and CT images. The DenseNet121 feature
extractor with Bagging tree classifier achieved the best performance with 99%
classification accuracy. The second-best learner was a hybrid of the a ResNet50
feature extractor trained by LightGBM with an accuracy of 98%.
