Advancing COVID-19 Detection in 3D CT Scans

Qingqiu Li, Runtian Yuan, Junlin Hou, Jilan Xu, Yuejie Zhang +2 more
3/18/2024
eess.IVcs.CV

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Abstract

To make a more accurate diagnosis of COVID-19, we propose a straightforward yet effective model. Firstly, we analyse the characteristics of 3D CT scans and remove the non-lung parts, facilitating the model to focus on lesion-related areas and reducing computational cost. We use ResNeSt50 as the strong feature extractor, initializing it with pretrained weights which have COVID-19-specific prior knowledge. Our model achieves a Macro F1 Score of 0.94 on the validation set of the 4th COV19D Competition Challenge $\mathrm{I}$, surpassing the baseline by 16%. This indicates its effectiveness in distinguishing between COVID-19 and non-COVID-19 cases, making it a robust method for COVID-19 detection.

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Cite this paper

@article{li2024advancing,
  title  = {Advancing COVID-19 Detection in 3D CT Scans},
  author = {Qingqiu Li and Runtian Yuan and Junlin Hou and Jilan Xu and Yuejie Zhang and Rui Feng and Hao Chen},
  year   = {2024},
  eprint = {2403.11953},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.11953v1}
}

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