  The COVID-19 pandemic continues to rage on, with multiple waves causing
substantial harm to health and economies around the world. Motivated by the use
of CT imaging at clinical institutes around the world as an effective
complementary screening method to RT-PCR testing, we introduced COVID-Net CT, a
neural network tailored for detection of COVID-19 cases from chest CT images as
part of the open source COVID-Net initiative. However, one potential limiting
factor is restricted quantity and diversity given the single nation patient
cohort used. In this study, we introduce COVID-Net CT-2, enhanced deep neural
networks for COVID-19 detection from chest CT images trained on the largest
quantity and diversity of multinational patient cases in research literature.
We introduce two new CT benchmark datasets, the largest comprising a
multinational cohort of 4,501 patients from at least 15 countries. We leverage
explainability to investigate the decision-making behaviour of COVID-Net CT-2,
with the results for select cases reviewed and reported on by two
board-certified radiologists with over 10 and 30 years of experience,
respectively. The COVID-Net CT-2 neural networks achieved accuracy, COVID-19
sensitivity, PPV, specificity, and NPV of 98.1%/96.2%/96.7%/99%/98.8% and
97.9%/95.7%/96.4%/98.9%/98.7%, respectively. Explainability-driven performance
validation shows that COVID-Net CT-2's decision-making behaviour is consistent
with radiologist interpretation by leveraging correct, clinically relevant
critical factors. The results are promising and suggest the strong potential of
deep neural networks as an effective tool for computer-aided COVID-19
assessment. While not a production-ready solution, we hope the open-source,
open-access release of COVID-Net CT-2 and benchmark datasets will continue to
enable researchers, clinicians, and citizen data scientists alike to build upon
them.
