YAbdelsatar / COVIDNet-CT

COVID-Net Open Source Initiative - Models for COVID-19 Detection from Chest CT

COVID-Net Open Source Initiative - COVIDNet-CT

Note: The COVIDNet-CT models provided here 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 (not meant for direct clinical diagnosis), and we are working continuously to improve them as new data becomes available. Please do not use COVIDNet-CT for self-diagnosis and seek help from your local health authorities.

Update 2020-12-03: We moved the COVIDx-CT v1 dataset to Kaggle

Update 2020-09-13: We released the COVIDNet-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 COVIDNet-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 CT imaging data collected by the China National Center for Bioinformation comprising 104,009 images across 1,489 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 COVIDNet-CT, and in doing so ensure that COVIDNet-CT makes predictions based on relevant indicators in CT images. Both COVIDNet-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 COVIDNet-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 COVIDNet-CT and a full description of the COVIDx-CT dataset, please click 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 [e-mail] and [e-mail] or [e-mail].

For COVIDNet-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 COVIDNet-CT results on your data or existing data, please reach out to [e-mail] or [e-mail].

If there are any technical questions after the README, FAQ, and past/current issues have been read, please post an issue or contact:

• [e-mail]
• [e-mail]
• [e-mail]

If you find our work useful, you can cite our paper using:

@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},
    year={forthcoming},
    doi={10.3389/fmed.2020.608525},
    url={https://doi.org/10.3389/fmed.2020.608525}
}

Core COVID-Net Team

• DarwinAI Corp., Canada and Vision and Image Processing Research Group, University of Waterloo, Canada
  • Linda Wang
  • Alexander Wong
  • Zhong Qiu Lin
  • Paul McInnis
  • Audrey Chung
  • Melissa Rinch
  • Maya Pavlova
  • Naomi Terhljan
  • Hayden Gunraj, COVIDNet for CT
  • Jeffer Peng, COVIDNet UI
• Vision and Image Processing Research Group, University of Waterloo, Canada
  • James Lee
  • Hossain Aboutaleb
• Ashkan Ebadi and Pengcheng Xi (National Research Council Canada)
• Kim-Ann Git (Selayang Hospital)
• Abdul Al-Haimi, COVID-19 ShuffleNet Chest X-Ray Model

Table of Contents

1. Requirements to install on your system
2. How to download and prepare COVIDx-CT dataset
3. Steps for training, evaluation and inference
4. Results
5. Links to pretrained models

Requirements

The main requirements are listed below:

• Tested with Tensorflow 1.15
• OpenCV 4.2.0
• Python 3.7
• Numpy
• Scikit-Learn
• Matplotlib

Results

These are the final test results for each COVIDNet-CT model on the COVIDx-CT dataset.

COVIDNet-CT-A

Confusion matrix for COVIDNet-CT-A on the COVIDx-CT test dataset.

Sensitivity (%)
Normal	Pneumonia	COVID-19
100	99.0	97.3

Positive Predictive Value (%)
Normal	Pneumonia	COVID-19
99.4	98.4	99.7

COVIDNet-CT-B

Confusion matrix for COVIDNet-CT-B on the COVIDx-CT test dataset.

Sensitivity (%)
Normal	Pneumonia	COVID-19
99.8	99.2	94.7

Positive Predictive Value (%)
Normal	Pneumonia	COVID-19
99.4	96.8	99.8