ArticleFrontiers in medicine2022
COVID-Net CXR-2: An Enhanced Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest X-ray Images.
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Adaptive Normalization Enhances the Generalization of Deep Learning Model in Chest X-Ray Classification.Journal of imaging · 2025Article
- Designing a computer-assisted diagnosis system for cardiomegaly detection and radiology report generation.PLOS digital health · 2025Article
- A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound.BMC medical imaging · 2024Review
- A medical multimodal large language model for future pandemics.NPJ digital medicine · 2023Article
- Augmentation strategies for an imbalanced learning problem on a novel COVID-19 severity dataset.Scientific reports · 2023Article
- Towards Building a Trustworthy Deep Learning Framework for Medical Image Analysis.Sensors (Basel, Switzerland) · 2023Article
- Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives.Medical image analysis · 2023Review
- A Systematic Literature Review and Future Perspectives for Handling Big Data Analytics in COVID-19 Diagnosis.New generation computing · 2023Article
- BND-VGG-19: A deep learning algorithm for COVID-19 identification utilizing X-ray images.Knowledge-based systems · 2022Article
- COVID-19 detection on Chest X-ray images: A comparison of CNN architectures and ensembles.Expert systems with applications · 2022Article
- COVID-19 prognosis using limited chest X-ray images.Applied soft computing · 2022Article
- Fine-Tuning Convolutional Neural Networks for COVID-19 Detection from Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2021Article
- MEDUSA: Multi-Scale Encoder-Decoder Self-Attention Deep Neural Network Architecture for Medical Image Analysis.Frontiers in medicine · 2021Article
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10 authors.
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Abstract
As the COVID-19 pandemic devastates 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. 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 and 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 behavior 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.
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