Advancing COVID-19 Detection in 3D CT Scans

arXiv:2403.11953v1 Announce Type: cross
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 …

abstract arxiv computational cost covid covid-19 cs.cv detection diagnosis eess.iv feature focus scans type