Note: The COVID-Net CT models provided here as part of the COVID-Net Initiative are intended to be used as reference models that can be built upon and enhanced as new data becomes available. They are currently at a research stage and not yet intended as production-ready models (i.e., not meant for direct clinical diagnosis), and we are working continuously to improve them as new data becomes available. Please do not use COVID-Net CT for self-diagnosis and seek help from your local health authorities.
Update 2022-06-02: We released the COVIDx CT-3A and CT-3B datasets on Kaggle, comprising 425,024 CT slices from 5,312 patients and 431,205 CT slices from 6,068 patients, respectively. The data is described in this preprint.
Update 2022-03-10: The COVID-Net CT-2 paper was published in Frontiers in Medicine.
Update 2021-01-26: We released the COVID-Net CT-2 models and COVIDx CT-2A and CT-2B datasets, comprising 194,922 CT slices from 3,745 patients and 201,103 CT slices from 4,501 patients, respectively.
Update 2020-12-23: The COVID-Net CT-1 paper was published in Frontiers in Medicine.
Update 2020-12-03: We released the COVIDx CT-1 dataset on Kaggle.
Update 2020-09-13: We released a preprint of the COVID-Net CT paper.
Example CT scans of COVID-19 cases and their associated critical factors (highlighted in red) as identified by GSInquire.
The coronavirus disease 2019 (COVID-19) pandemic continues to have a tremendous impact on patients and healthcare systems around the world. In the fight against this novel disease, there is a pressing need for rapid and effective screening tools to identify patients infected with COVID-19, and to this end CT imaging has been proposed as one of the key screening methods which may be used as a complement to RT-PCR testing, particularly in situations where patients undergo routine CT scans for non-COVID-19 related reasons, patients have worsening respiratory status or developing complications that require expedited care, or patients are suspected to be COVID-19-positive but have negative RT-PCR test results. Early studies on CT-based screening have reported abnormalities in chest CT images which are characteristic of COVID-19 infection, but these abnormalities may be difficult to distinguish from abnormalities caused by other lung conditions. Motivated by this, in this study we introduce COVID-Net CT, a deep convolutional neural network architecture that is tailored for detection of COVID-19 cases from chest CT images via a machine-driven design exploration approach. Additionally, we introduce COVIDx CT, a benchmark CT image dataset derived from a variety of sources of CT imaging data currently comprising 201,103 images across 4,501 patient cases. Furthermore, in the interest of reliability and transparency, we leverage an explainability-driven performance validation strategy to investigate the decision-making behaviour of COVID-Net CT, and in doing so ensure that COVID-Net CT makes predictions based on relevant indicators in CT images. Both COVID-Net CT and the COVIDx CT dataset are available to the general public in an open-source and open access manner as part of the COVID-Net Initiative. While COVID-Net CT is not yet a production-ready screening solution, we hope that releasing the model and dataset will encourage researchers, clinicians, and citizen data scientists alike to leverage and build upon them.
For a detailed description of the methodology behind COVID-Net CT and a full description of the COVIDx CT dataset, please read the COVID-Net CT-1 and COVID-Net CT-2 papers.
This work is made possible by a number of publicly available CT data sources. Licenses and acknowledgements for these datasets can be found here.
Our desire is to encourage broad adoption and contribution to this project. Accordingly this project has been licensed under the GNU Affero General Public License 3.0. Please see license file for terms. If you would like to discuss alternative licensing models, please reach out to us at [email protected] and [email protected].
For COVID-Net CXR models and the COVIDx dataset for COVID-19 detection and severity assessment from chest X-ray images, please go to the main COVID-Net repository.
If you are a researcher or healthcare worker and you would like access to the GSInquire tool to use to interpret COVID-Net CT results on your data or existing data, please reach out to [email protected] or [email protected].
If there are any technical questions after the README, FAQ, and past/current issues have been read, please post an issue or contact [email protected]
If you find our work useful for your research, please cite:
@article{Gunraj2020,
author={Gunraj, Hayden and Wang, Linda and Wong, Alexander},
title={COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest CT Images},
journal={Frontiers in Medicine},
volume={7},
pages={1025},
year={2020},
url={https://www.frontiersin.org/article/10.3389/fmed.2020.608525},
doi={10.3389/fmed.2020.608525},
issn={2296-858X}
}
@article{Gunraj2022,
author={Gunraj, Hayden and Sabri, Ali and Koff, David and Wong, Alexander},
title={COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 From Chest CT Images Through Bigger, More Diverse Learning},
journal={Frontiers in Medicine},
volume={8},
pages={729287},
year={2022},
url={https://www.frontiersin.org/articles/10.3389/fmed.2021.729287},
doi={10.3389/fmed.2021.729287},
issn={2296-858X}
}
- DarwinAI Corp., Canada and Vision and Image Processing Lab, University of Waterloo, Canada
- Linda Wang
- Alexander Wong
- Zhong Qiu Lin
- Paul McInnis
- Audrey Chung
- Melissa Rinch
- Jeffer Peng
- Vision and Image Processing Lab, University of Waterloo, Canada
- James Lee
- Hossein Aboutalebi
- Alex MacLean
- Saad Abbasi
- Hayden Gunraj
- Maya Pavlova
- Naomi Terhljan
- Siddharth Surana
- Andy Zhao
- Ashkan Ebadi and Pengcheng Xi (National Research Council Canada)
- Kim-Ann Git (Selayang Hospital)
- Abdul Al-Haimi, COVID-19 ShuffleNet Chest X-Ray Model
- Dr. Ali Sabri (Department of Radiology, Niagara Health, McMaster University, Canada)
- Requirements to install on your system
- How to download and prepare the COVIDx CT dataset
- Steps for training, evaluation and inference
- Results
- Links to pretrained models
- Licenses and acknowledgements for the datasets used
The main requirements are listed below:
- Tested with Tensorflow 1.15
- OpenCV 4.2.0
- Python 3.7
- Numpy
- Scikit-Learn
- Matplotlib
These are the final test results for the current COVID-Net CT models on the COVIDx CT dataset.
Confusion matrix for COVID-Net CT-2 L on the COVIDx CT-3A test dataset.
| Sensitivity (%) | ||
|---|---|---|
| Normal | Pneumonia | COVID-19 |
| 99.0 | 98.2 | 96.2 |
| Positive Predictive Value (%) | ||
|---|---|---|
| Normal | Pneumonia | COVID-19 |
| 99.4 | 97.2 | 96.7 |
Confusion matrix for COVID-Net CT-2 S on the COVIDx CT-3A test dataset.
| Sensitivity (%) | ||
|---|---|---|
| Normal | Pneumonia | COVID-19 |
| 98.9 | 98.1 | 95.7 |
| Positive Predictive Value (%) | ||
|---|---|---|
| Normal | Pneumonia | COVID-19 |
| 99.3 | 97.0 | 96.4 |
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