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arXiv · 2107.04808

COVID Detection in Chest CTs: Improving the Baseline on COV19-CT-DB

Radu Miron·Cosmin Moisii·Sergiu Dinu·Mihaela Breaban
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Abstract

The paper presents a comparative analysis of three distinct approaches based on deep learning for COVID-19 detection in chest CTs. The first approach is a volumetric one, involving 3D convolutions, while the other two approaches perform at first slice-wise classification and then aggregate the results at the volume level. The experiments are carried on the COV19-CT-DB dataset, with the aim of addressing the challenge raised by the MIA-COV19D Competition within ICCV 2021. Our best results on the validation subset reach a macro-F1 score of 0.92, which improves considerably the baseline score of 0.70 set by the organizers.

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Radu Miron, Cosmin Moisii, Sergiu Dinu, Mihaela Breaban. 2021-07-10. COVID Detection in Chest CTs: Improving the Baseline on COV19-CT-DB. https://arxiv.org/abs/2107.04808

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