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

arXiv: https://arxiv.org/abs/2101.07433 · HTML: https://mlsanity.com/paper/2101.07433/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 73 references that are in the map.
- First posted on arXiv 19 Jan 2021.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images](https://mlsanity.com/paper/2003.09871/) (2020)
- [MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset](https://mlsanity.com/paper/2005.06465/) (2020)
- [Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation](https://mlsanity.com/paper/2004.12537/) (2020)
- [COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest CT Images](https://mlsanity.com/paper/2009.05383/) (2020)
- [Do Explanations Reflect Decisions? A Machine-centric Strategy to Quantify the Performance of Explainability Algorithms](https://mlsanity.com/paper/1910.07387/) (2019)
- [NetScore: Towards Universal Metrics for Large-scale Performance Analysis of Deep Neural Networks for Practical On-Device Edge Usage](https://mlsanity.com/paper/1806.05512/) (2018)
- [FermiNets: Learning generative machines to generate efficient neural networks via generative synthesis](https://mlsanity.com/paper/1809.05989/) (2018)
- [COVID-Net S: Towards computer-aided severity assessment via training and validation of deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity](https://mlsanity.com/paper/2005.12855/) (2020)
- [A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)](https://mlsanity.com/paper/2008.04815/) (2020)
- [Recognizing Car Fluents from Video](https://mlsanity.com/paper/1603.08067/) (2016)
- [Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks](https://mlsanity.com/paper/1704.04133/) (2017)
- [Discovery Radiomics with CLEAR-DR: Interpretable Computer Aided Diagnosis of Diabetic Retinopathy](https://mlsanity.com/paper/1710.10675/) (2017)

## Built on by (most cited first, 29 of 29)

- [Transformers in Medical Imaging: A Survey](https://mlsanity.com/paper/2201.09873/) (2022)
- [Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives](https://mlsanity.com/paper/2206.01136/) (2022)
- [COVID-Net CXR-2: An Enhanced Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-ray Images](https://mlsanity.com/paper/2105.06640/) (2021)
- [Vision Transformers in Medical Computer Vision -- A Contemplative Retrospection](https://mlsanity.com/paper/2203.15269/) (2022)
- [Curia: A Multi-Modal Foundation Model for Radiology](https://mlsanity.com/paper/2509.06830/) (2025)
- [TB-Net: A Tailored, Self-Attention Deep Convolutional Neural Network Design for Detection of Tuberculosis Cases from Chest X-ray Images](https://mlsanity.com/paper/2104.03165/) (2021)
- [Vision Transformers in Medical Imaging: A Review](https://mlsanity.com/paper/2211.10043/) (2022)
- [COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images](https://mlsanity.com/paper/2105.00256/) (2021)
- [A 3D CNN Network with BERT For Automatic COVID-19 Diagnosis From CT-Scan Images](https://mlsanity.com/paper/2106.14403/) (2021)
- [Optimising Chest X-Rays for Image Analysis by Identifying and Removing Confounding Factors](https://mlsanity.com/paper/2208.10320/) (2022)
- [Curia-2: Scaling Self-Supervised Learning for Radiology Foundation Models](https://mlsanity.com/paper/2604.01987/) (2026)
- [Data Augmentation and CNN Classification For Automatic COVID-19 Diagnosis From CT-Scan Images On Small Dataset](https://mlsanity.com/paper/2108.07148/) (2021)
- [COVID-Net USPro: An Open-Source Explainable Few-Shot Deep Prototypical Network to Monitor and Detect COVID-19 Infection from Point-of-Care Ultrasound Images](https://mlsanity.com/paper/2301.01679/) (2023)
- [Interpretable Medical Imagery Diagnosis with Self-Attentive Transformers: A Review of Explainable AI for Health Care](https://mlsanity.com/paper/2309.00252/) (2023)
- [Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining](https://mlsanity.com/paper/2605.21906/) (2026)
- [COVID-19 Detection from Chest X-ray Images using Imprinted Weights Approach](https://mlsanity.com/paper/2105.01710/) (2021)
- [COVID-Net US: A Tailored, Highly Efficient, Self-Attention Deep Convolutional Neural Network Design for Detection of COVID-19 Patient Cases from Point-of-care Ultrasound Imaging](https://mlsanity.com/paper/2108.03131/) (2021)
- [PrepNet: A Convolutional Auto-Encoder to Homogenize CT Scans for Cross-Dataset Medical Image Analysis](https://mlsanity.com/paper/2208.09408/) (2022)
- [Fibrosis-Net: A Tailored Deep Convolutional Neural Network Design for Prediction of Pulmonary Fibrosis Progression from Chest CT Images](https://mlsanity.com/paper/2103.04008/) (2021)
- [COVID-Net CT-S: 3D Convolutional Neural Network Architectures for COVID-19 Severity Assessment using Chest CT Images](https://mlsanity.com/paper/2105.01284/) (2021)
- [Insights into Data through Model Behaviour: An Explainability-driven Strategy for Data Auditing for Responsible Computer Vision Applications](https://mlsanity.com/paper/2106.09177/) (2021)
- [COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT Images](https://mlsanity.com/paper/2206.03043/) (2022)
- [From pre-training to downstream performance: Does domain-specific pre-training make sense?](https://mlsanity.com/paper/2605.08819/) (2026)
- [COVID-Net MLSys: Designing COVID-Net for the Clinical Workflow](https://mlsanity.com/paper/2109.06421/) (2021)
- [Improving COVID-19 CT Classification of CNNs by Learning Parameter-Efficient Representation](https://mlsanity.com/paper/2208.04718/) (2022)
- [DCNNV-19: A Deep Convolutional Neural Network for COVID-19 Detection in Chest Computed Tomographies](https://mlsanity.com/paper/2208.09349/) (2022)
- [GeMix: Conditional GAN-Based Mixup for Improved Medical Image Augmentation](https://mlsanity.com/paper/2507.15577/) (2025)
- [Vision-Language Model Based Multi-Expert Fusion for CT Image Classification](https://mlsanity.com/paper/2603.15154/) (2026)
- [Demographically-Conditioned Synthetic Medical Images for Bias Mitigation and Bias Detection in Disease Classifiers](https://mlsanity.com/paper/2607.14984/) (2026)

## Abstract (arXiv)

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.