COVID-19 Detection Using Chest X-Ray
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).
Please refer to INSTALL.md for installation.
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.
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 |
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.
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 |  |  |
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.
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This work was collaboratively conducted by Arpan Mangal, Surya Kalia, Harish Rajgopal, Krithika Rangarajan, Vinay Namboodiri, Subhashis Banerjee and Chetan Arora.
@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}}
If you have any question, please file an issue or contact the author:
Arpan Mangal: mangalarpan@gmail.comSurya Kalia: suryackalia@gmail.com
torch>=1.0