Advancing COVID-19 Detection in 3D CT Scans
<|reference_start|>Advancing COVID-19 Detection in 3D CT Scans: 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.<|reference_end|>
@article{li2024advancing,
    title={Advancing COVID-19 Detection in 3D CT Scans},
    author={Qingqiu Li, Runtian Yuan, Junlin Hou, Jilan Xu, Yuejie Zhang, Rui
  Feng, Hao Chen},
    journal={arXiv preprint arXiv:2403.11953},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11953},
    primaryClass={eess.IV cs.CV}
}