Advancing COVID-19 Detection in 3D CT Scans
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866913269999992832 |
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| author | Li, Qingqiu Yuan, Runtian Hou, Junlin Xu, Jilan Zhang, Yuejie Feng, Rui Chen, Hao |
| author_facet | Li, Qingqiu Yuan, Runtian Hou, Junlin Xu, Jilan Zhang, Yuejie Feng, Rui Chen, Hao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_11953 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advancing COVID-19 Detection in 3D CT Scans Li, Qingqiu Yuan, Runtian Hou, Junlin Xu, Jilan Zhang, Yuejie Feng, Rui Chen, Hao Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Advancing COVID-19 Detection in 3D CT Scans |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.11953 |