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

This is one of the scientific articles published by one or more synbrAIn collaborators and data scientists.

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