CovidAID for Detection of COVID-19 from X-Ray Images

We present CovidAID (Covid AI Detector), a PyTorch (python3) based implementation, to identify COVID-19 cases from X-Ray images. The model takes as input a chest X-Ray image and outputs the probability scores for 4 classes (NORMAL, Bacterial Pneumonia, Viral Pneumonia and COVID-19).

It is based on CheXNet (and it's reimplementation by arnoweng).

Installation

Please refer to INSTALL.md for installation.

Dataset

CovidAID uses the covid-chestxray-dataset for COVID-19 X-Ray images and chest-xray-pneumonia dataset for data on Pneumonia and Normal lung X-Ray images.

Data Distribution

Chest X-Ray image distribution | Type | Normal | Bacterial Pneumonia | Viral Pneumonia | COVID-19 | Total | |:-----:|:------:|:---------:|:--------:|:--------:|:-----:| | Train | 1341 | 2530 | 1337 | 115 | 5323 | | Val | 8 | 8 | 8 | 10 | 34 | Test | 234 | 242 | 148 | 30 | 654 |

Chest X-Ray patient distribution | Type | Normal | Bacterial Pneumonia | Viral Pneumonia | COVID-19 | Total | |:-----:|:------:|:---------:|:--------:|:--------:|:-----:| | Train | 1000 | 1353 | 1083 | 80 | 3516 | | Val | 8 | 7 | 7 | 7 | 29 | Test | 202 | 77 | 126 | 19 | 424 |

Get started

Please refer our paper paper for description of architecture and method. Refer to GETTING_STARTED.md for detailed examples and abstract usage for training the models and running inference.

Results

We present the results in terms of both the per-class AUROC (Area under ROC curve) on the lines of CheXNet, as well as confusion matrix formed by treating the most confident class prediction as the final prediction. We obtain a mean AUROC of 0.9738 (4-class configuration).

<center> <table> <tr><th></th><th>3-Class Classification</th><th>4-Class Classification</th></tr> <tr> <td></td> <td>

| Pathology | AUROC | Sensitivity | PPV | :--------: | :--------: | :--------: | :--------: | | Normal Lung | 0.9795 | 0.744 | 0.989 | Bacterial Pneumonia | 0.9814 | 0.995 | 0.868 | COVID-19 | 0.9997 | 1.000 | 0.968

</td><td>

| Pathology | AUROC | Sensitivity | PPV | :--------: | :--------: | :--------: | :--------: | | Normal Lung | 0.9788 | 0.761 | 0.989 | Bacterial Pneumonia | 0.9798 | 0.961 | 0.881 | Viral Pneumonia | 0.9370 | 0.872 | 0.721 | COVID-19 | 0.9994 | 1.000 | 0.938

</td></tr> <tr> <td>ROC curve</td> <td>

</td><td>

</td> </tr> <tr> <td>Confusion Matrix</td> <td>

</td><td>

</td> </tr> </table> </center>

Visualizations

To demonstrate the results qualitatively, we generate saliency maps for our model’s predictions using RISE. The purpose of these visualizations was to have an additional check to rule out model over-fitting as well as to validate whether the regions of attention correspond to the right features from a radiologist’s perspective. Below are some of the saliency maps on COVID-19 positive X-rays.

<center> <table> <tr> <td>

</td><td>

</td><td>

</td></tr> <tr><td>

</td><td>

</td><td>

</td></tr> </table> </center>

Contributions

This work was collaboratively conducted by Arpan Mangal, Surya Kalia, Harish Rajgopal, Krithika Rangarajan, Vinay Namboodiri, Subhashis Banerjee and Chetan Arora.

Citation

@article{covidaid,
    title={CovidAID: COVID-19 Detection Using ChestX-Ray},
    author={Arpan Mangal and Surya Kalia and Harish Rajgopal and Krithika Rangarajan and Vinay Namboodiri and Subhashis Banerjee and Chetan Arora},
    year={2020},
    journal={arXiv 2004.09803},
    url={https://github.com/arpanmangal/CovidAID}
}

Contact

If you have any question, please file an issue or contact the author:

Arpan Mangal: mangalarpan@gmail.com
Surya Kalia: suryackalia@gmail.com

TODO

• Add support for torch>=1.0
• Support for multi-GPU training