  COVID-19 pandemic continues to spread rapidly over the world and causes a
tremendous crisis in global human health and the economy. Its early detection
and diagnosis are crucial for controlling the further spread. Many deep
learning-based methods have been proposed to assist clinicians in automatic
COVID-19 diagnosis based on computed tomography imaging. However, challenges
still remain, including low data diversity in existing datasets, and
unsatisfied detection resulting from insufficient accuracy and sensitivity of
deep learning models. To enhance the data diversity, we design augmentation
techniques of incremental levels and apply them to the largest open-access
benchmark dataset, COVIDx CT-2A. Meanwhile, similarity regularization (SR)
derived from contrastive learning is proposed in this study to enable CNNs to
learn more parameter-efficient representations, thus improving the accuracy and
sensitivity of CNNs. The results on seven commonly used CNNs demonstrate that
CNN performance can be improved stably through applying the designed
augmentation and SR techniques. In particular, DenseNet121 with SR achieves an
average test accuracy of 99.44% in three trials for three-category
classification, including normal, non-COVID-19 pneumonia, and COVID-19
pneumonia. And the achieved precision, sensitivity, and specificity for the
COVID-19 pneumonia category are 98.40%, 99.59%, and 99.50%, respectively. These
statistics suggest that our method has surpassed the existing state-of-the-art
methods on the COVIDx CT-2A dataset.

Image and Video Processing;Computer Vision and Pattern Recognition