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 (coming soon) 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 | |||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
| |||||||||||||||||||||||||||||||||||||
| 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.
|
|
|
|
|
|
|
|
This work was collaboratively conducted by Arpan Mangal, Surya Kalia, Harish Rajgopal, Krithika Rangarajan, Vinay Namboodiri, Subhashis Banerjee and Chetan Arora.
If you have any question, please file an issue or contact the author:
Arpan Mangal: mangalarpan@gmail.com
- Add support for
torch>=1.0 - Support for multi-GPU training