# COVID-Net CXR-2: An Enhanced Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-ray Images

arXiv: https://arxiv.org/abs/2105.06640 · HTML: https://mlsanity.com/paper/2105.06640/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 77 references that are in the map.
- First posted on arXiv 14 May 2021.

## Builds on (most cited first, 30 of 30)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (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)
- [COVID-19 Image Data Collection](https://mlsanity.com/paper/2003.11597/) (2020)
- [Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks](https://mlsanity.com/paper/2003.11617/) (2020)
- [BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients](https://mlsanity.com/paper/2006.01174/) (2020)
- [Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learning](https://mlsanity.com/paper/2004.09363/) (2020)
- [Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network](https://mlsanity.com/paper/2003.13815/) (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)
- [COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Images](https://mlsanity.com/paper/2004.02696/) (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)
- [COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse Learning](https://mlsanity.com/paper/2101.07433/) (2021)
- [COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs](https://mlsanity.com/paper/2003.14395/) (2020)
- [FermiNets: Learning generative machines to generate efficient neural networks via generative synthesis](https://mlsanity.com/paper/1809.05989/) (2018)
- [Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data](https://mlsanity.com/paper/2004.05405/) (2020)
- [COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19](https://mlsanity.com/paper/2005.01577/) (2020)
- [COVIDx-US -- An open-access benchmark dataset of ultrasound imaging data for AI-driven COVID-19 analytics](https://mlsanity.com/paper/2103.10003/) (2021)
- [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)
- [Towards an Effective and Efficient Deep Learning Model for COVID-19 Patterns Detection in X-ray Images](https://mlsanity.com/paper/2004.05717/) (2020)
- [COVID-FACT: A Fully-Automated Capsule Network-based Framework for Identification of COVID-19 Cases from Chest CT scans](https://mlsanity.com/paper/2010.16041/) (2020)
- [DeepCOVIDExplainer: Explainable COVID-19 Diagnosis Based on Chest X-ray Images](https://mlsanity.com/paper/2004.04582/) (2020)
- [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)
- [A Cascaded Learning Strategy for Robust COVID-19 Pneumonia Chest X-Ray Screening](https://mlsanity.com/paper/2004.12786/) (2020)
- [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)
- [COVID-19 Detection from Chest X-ray Images using Imprinted Weights Approach](https://mlsanity.com/paper/2105.01710/) (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)

## Built on by (most cited first, 22 of 22)

- [Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data](https://mlsanity.com/paper/2308.02463/) (2023)
- [MedKLIP: Medical Knowledge Enhanced Language-Image Pre-Training in Radiology](https://mlsanity.com/paper/2301.02228/) (2023)
- [Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives](https://mlsanity.com/paper/2206.01136/) (2022)
- [Decomposing Disease Descriptions for Enhanced Pathology Detection: A Multi-Aspect Vision-Language Pre-training Framework](https://mlsanity.com/paper/2403.07636/) (2024)
- [Medical Vision Language Pretraining: A survey](https://mlsanity.com/paper/2312.06224/) (2023)
- [HarsanyiNet: Computing Accurate Shapley Values in a Single Forward Propagation](https://mlsanity.com/paper/2304.01811/) (2023)
- [Large-scale Long-tailed Disease Diagnosis on Radiology Images](https://mlsanity.com/paper/2312.16151/) (2023)
- [A Comprehensive Survey on Evaluating Large Language Model Applications in the Medical Industry](https://mlsanity.com/paper/2404.15777/) (2024)
- [Large Language Model Benchmarks in Medical Tasks](https://mlsanity.com/paper/2410.21348/) (2024)
- [COVIDx CXR-3: A Large-Scale, Open-Source Benchmark Dataset of Chest X-ray Images for Computer-Aided COVID-19 Diagnostics](https://mlsanity.com/paper/2206.03671/) (2022)
- [MEDUSA: Multi-scale Encoder-Decoder Self-Attention Deep Neural Network Architecture for Medical Image Analysis](https://mlsanity.com/paper/2110.06063/) (2021)
- [SPCXR: Self-supervised Pretraining using Chest X-rays Towards a Domain Specific Foundation Model](https://mlsanity.com/paper/2211.12944/) (2022)
- [K-Diag: Knowledge-enhanced Disease Diagnosis in Radiographic Imaging](https://mlsanity.com/paper/2302.11557/) (2023)
- [LLaVA-RadZ: Can Multimodal Large Language Models Effectively Tackle Zero-shot Radiology Recognition?](https://mlsanity.com/paper/2503.07487/) (2025)
- [What is Your Metric Telling You? Evaluating Classifier Calibration under Context-Specific Definitions of Reliability](https://mlsanity.com/paper/2205.11454/) (2022)
- [Masks and Manuscripts: Advancing Medical Pre-training with End-to-End Masking and Narrative Structuring](https://mlsanity.com/paper/2407.16264/) (2024)
- [A Trustworthy Framework for Medical Image Analysis with Deep Learning](https://mlsanity.com/paper/2212.02764/) (2022)
- [Distribution-Based Masked Medical Vision-Language Model Using Structured Reports](https://mlsanity.com/paper/2507.21794/) (2025)
- [KEPIL: Knowledge-Enhanced Prompt-Image Learning for Prompt-Robust Disease Detection](https://mlsanity.com/paper/2605.09132/) (2026)
- [COVID-19 Detection on Chest X-Ray Images: A comparison of CNN architectures and ensembles](https://mlsanity.com/paper/2111.09972/) (2021)
- [Study of Vision Transformers for Covid-19 Detection from Chest X-rays](https://mlsanity.com/paper/2307.09402/) (2023)
- [A Comprehensive Analysis of COVID-19 Detection Using Bangladeshi Data and Explainable AI](https://mlsanity.com/paper/2506.07234/) (2025)

## Abstract (arXiv)

As the COVID-19 pandemic continues to devastate globally, the use of chest X-ray (CXR) imaging as a complimentary screening strategy to RT-PCR testing continues to grow given its routine clinical use for respiratory complaint. As part of the COVID-Net open source initiative, we introduce COVID-Net CXR-2, an enhanced deep convolutional neural network design for COVID-19 detection from CXR images built using a greater quantity and diversity of patients than the original COVID-Net. To facilitate this, we also introduce a new benchmark dataset composed of 19,203 CXR images from a multinational cohort of 16,656 patients from at least 51 countries, making it the largest, most diverse COVID-19 CXR dataset in open access form. The COVID-Net CXR-2 network achieves sensitivity and positive predictive value of 95.5%/97.0%, respectively, and was audited in a transparent and responsible manner. Explainability-driven performance validation was used during auditing to gain deeper insights in its decision-making behaviour and to ensure clinically relevant factors are leveraged for improving trust in its usage. Radiologist validation was also conducted, where select cases were reviewed and reported on by two board-certified radiologists with over 10 and 19 years of experience, respectively, and showed that the critical factors leveraged by COVID-Net CXR-2 are consistent with radiologist interpretations. While not a production-ready solution, we hope the open-source, open-access release of COVID-Net CXR-2 and the respective CXR benchmark dataset will encourage researchers, clinical scientists, and citizen scientists to accelerate advancements and innovations in the fight against the pandemic.