Advancing COVID-19 Detection in 3D CT Scans
CoRR(2024)
摘要
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 I, surpassing the
baseline by 16
COVID-19 and non-COVID-19 cases, making it a robust method for COVID-19
detection.
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