Chest X-Ray COVID-19 Detection

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Work presented at the Ethics and Explainability for Responsible Data Science (EE-RDS) Conference 2021.

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J. B. Pal and N. Paul, "Classifying Chest X-Ray COVID-19 images via Transfer Learning," 2021 Ethics and Explainability for Responsible Data Science (EE-RDS), 2021, pp. 1-8, doi: 10.1109/EE-RDS53766.2021.9708580.

[ Video recording of presentation ] | [ Slides ] | [ Paper ]

Abstract

The internal behavior of Deep Neural Network architectures can be difficult to interpret. Certain architectures achieve impressive feats in a particular dataset while failing to show comparable performance in other datasets. Developing an architecture that performs well on a dataset can be a time-consuming affair and computationally intensive process. This study explains the effect of transfer learning by fine-tuning already available state-of-the-art architectures in different datasets and using them to classify Chest X-Ray images with high accuracy. Using transfer learning helps the model learn problem-specific features in a short period. It further shows that different models perform differently in a particular setting for a dataset. Ablation studies show that a combination of smaller structures that gives an overall better result may not give the best result in the combined model. In addition, the “belief” of the model for selecting a particular class is visualized in this study.

Download related models and datasets

https://figshare.com/articles/dataset/CXR_Challenge_QUBIQ_21_COVID19_Perc_Estimation/21225287

Note: (While using the model, use these labels)

index = {'normal': 0, 'covid': 1,  'pneumonia': 2}
rev_index = {0: 'normal',1: 'covid', 2: 'pneumonia'}

Some relevant stuffs from paper

Acknowledgements

The authors are thankful to Moulay Akhloufi for sharing the datasets.

The authors are also grateful to Swathy Prabhu Mj, Ramakrishna Mission Vivekananda Educational and Research Institute, for arranging a machine with an Asus RTX 2080 Ti (12 GB VRAM) and 64 GB RAM, to hasten the research.

If you find this work useful, please consider citing

Our paper

@INPROCEEDINGS{9708580,  
  author={Pal, Jimut Bahan and Paul, Nilayan},  
  booktitle={2021 Ethics and Explainability for Responsible Data Science (EE-RDS)},   
  title={Classifying Chest X-Ray COVID-19 images via Transfer Learning},   
  year={2021},  
  volume={},  
  number={},  
  pages={1-8},  
  doi={10.1109/EE-RDS53766.2021.9708580}
  }

For Dataset

@misc{CovidGrandChallenge2021,
  author = {Akhloufi, Moulay A. and Chetoui, Mohamed},
  title = {{Chest XR COVID-19 detection}},  
  howpublished = {\url{https://cxr-covid19.grand-challenge.org/}},
  month = {August},
  year = {2021},
  note = {Online; accessed September 2021},
  }