A Novel Abnormality Annotation Database for COVID-19 Affected Frontal Lung X-rays

  1. Surbhi Mittal
  2. Vasantha Kumar Venugopal
  3. Vikash Kumar Agarwal
  4. Manu Malhotra
  5. Jagneet Singh Chatha
  6. Savinay Kapur
  7. Ankur Gupta
  8. Vikas Batra
  9. Puspita Majumdar
  10. Aakarsh Malhotra
  11. Kartik Thakral
  12. Saheb Chhabra
  13. Mayank Vatsa
  14. Richa Singh
  15. Santanu Chaudhury
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Abstract

Purpose

To advance the usage of CXRs as a viable solution for efficient COVID-19 diagnostics by providing large-scale annotations of the abnormalities in frontal CXRs in BIMCV-COVID19+ database, and to provide a robust evaluation mechanism to facilitate its usage.

Materials and Methods

We provide the abnormality annotations in frontal CXRs by creating bounding boxes. The frontal CXRs are a part of the existing BIMCV-COVID19+ database. We also define four different protocols for robust evaluation of semantic segmentation and classification algorithms. Finally, we benchmark the defined protocols and report the results using popular deep learning models as a part of this study.

Results

For semantic segmentation, Mask-RCNN performs the best among all the models with a DICE score of 0.43 ± 0.01. For classification, we observe that MobileNetv2 yields the best results for 2-class and 3-class classification. We also observe that deep models report a lower performance for classifying other classes apart from the COVID class.

Conclusion

By making the annotated data and protocols available to the scientific community, we aim to advance the usage of CXRs as a viable solution for efficient COVID-19 diagnostics. This large-scale data will be useful for ML algorithms and can be used for learning radiological patterns observed in COVID-19 patients. Further, the protocols will facilitate ML practitioners for unified large-scale evaluation of their algorithms.

Data Availability Statement

The data associated with this work is available here : <underline>Radiologists’ Annotations on COVID-19+ X-rays <ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://osf.io/b35xu/">https://osf.io/b35xu/</ext-link> via @OSFramework and</underline><ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="http://covbase4all.igib.res.in/">http://covbase4all.igib.res.in/</ext-link>.

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