  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.
