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

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arxiv 2107.04808 v2 pith:S65PPYYT submitted 2021-07-10 eess.IV cs.CV

Radu Miron , Cosmin Moisii , Sergiu Dinu , Mihaela Breaban This is my paper · ORCID
classification eess.IVcs.CV
keywords approachesbaselinechestcov19-ct-dbdetectionfirstresultsscore
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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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