# 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

arXiv: https://arxiv.org/abs/2005.12855 · HTML: https://mlsanity.com/paper/2005.12855/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2020.

## Built on by (most cited first, 14 of 14)

- [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-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 Transformer using Low-level Chest X-ray Feature Corpus for COVID-19 Diagnosis and Severity Quantification](https://mlsanity.com/paper/2104.07235/) (2021)
- [RadioTransformer: A Cascaded Global-Focal Transformer for Visual Attention-guided Disease Classification](https://mlsanity.com/paper/2202.11781/) (2022)
- [COVIDx-US -- An open-access benchmark dataset of ultrasound imaging data for AI-driven COVID-19 analytics](https://mlsanity.com/paper/2103.10003/) (2021)
- [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)
- [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)
- [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 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)
- [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)
- [COVID-Net MLSys: Designing COVID-Net for the Clinical Workflow](https://mlsanity.com/paper/2109.06421/) (2021)

## Abstract (arXiv)

Background: A critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of the COVID-19 pandemic, is the assessment of the severity of disease progression. Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two important assessment metrics being extent of lung involvement and degree of opacity. In this proof-of-concept study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system. Materials and Methods: Data consisted of 396 CXRs from SARS-CoV-2 positive patient cases. Geographic extent and opacity extent were scored by two board-certified expert chest radiologists (with 20+ years of experience) and a 2nd-year radiology resident. The deep neural networks used in this study, which we name COVID-Net S, are based on a COVID-Net network architecture. 100 versions of the network were independently learned (50 to perform geographic extent scoring and 50 to perform opacity extent scoring) using random subsets of CXRs from the study, and we evaluated the networks using stratified Monte Carlo cross-validation experiments. Findings: The COVID-Net S deep neural networks yielded R2 of 0.664 ± 0.032 and 0.635 ± 0.044 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively, in stratified Monte Carlo cross-validation experiments. The best performing networks achieved R2 of 0.739 and 0.741 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively. Interpretation: The results are promising and suggest that the use of deep neural networks on CXRs could be an effective tool for computer-aided assessment of SARS-CoV-2 lung disease severity, although additional studies are needed before adoption for routine clinical use.