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The COVID-19 pandemic continues to rage on, with multiple waves causingsubstantial harm to health and economies around the world. Motivated by the useof CT imaging at clinical institutes around the world as an effectivecomplementary screening method to RT-PCR testing, we introduced COVID-Net CT, aneural network tailored for detection of COVID-19 cases from chest CT images aspart of the open source COVID-Net initiative. However, one potential limitingfactor is restricted quantity and diversity given the single nation patientcohort used. In this study, we introduce COVID-Net CT-2, enhanced deep neuralnetworks for COVID-19 detection from chest CT images trained on the largestquantity and diversity of multinational patient cases in research literature.We introduce two new CT benchmark datasets, the largest comprising amultinational cohort of 4,501 patients from at least 15 countries. We leverageexplainability to investigate the decision-making behaviour of COVID-Net CT-2,with the results for select cases reviewed and reported on by twoboard-certified radiologists with over 10 and 30 years of experience,respectively. The COVID-Net CT-2 neural networks achieved accuracy, COVID-19sensitivity, PPV, specificity, and NPV of 98.1%/96.2%/96.7%/99%/98.8% and97.9%/95.7%/96.4%/98.9%/98.7%, respectively. Explainability-driven performancevalidation shows that COVID-Net CT-2's decision-making behaviour is consistentwith radiologist interpretation by leveraging correct, clinically relevantcritical factors. The results are promising and suggest the strong potential ofdeep neural networks as an effective tool for computer-aided COVID-19assessment. While not a production-ready solution, we hope the open-source,open-access release of COVID-Net CT-2 and benchmark datasets will continue toenable researchers, clinicians, and citizen data scientists alike to build uponthem.

COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse Learning