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).

	3-Class Classification	4-Class Classification
	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	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
ROC curve
Confusion Matrix

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.

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