  Since the appearance of Covid-19 in late 2019, Covid-19 has become an active
research topic for the artificial intelligence (AI) community. One of the most
interesting AI topics is Covid-19 analysis of medical imaging. CT-scan imaging
is the most informative tool about this disease. This work is part of the 2nd
COV19D competition, where two challenges are set: Covid-19 Detection and
Covid-19 Severity Detection from the CT-scans. For Covid-19 detection from
CT-scans, we proposed an ensemble of 2D Convolution blocks with Densenet-161
models. Here, each 2D convolutional block with Densenet-161 architecture is
trained separately and in testing phase, the ensemble model is based on the
average of their probabilities. On the other hand, we proposed an ensemble of
Convolutional Layers with Inception models for Covid-19 severity detection. In
addition to the Convolutional Layers, three Inception variants were used,
namely Inception-v3, Inception-v4 and Inception-Resnet. Our proposed approaches
outperformed the baseline approach in the validation data of the 2nd COV19D
competition by 11% and 16% for Covid-19 detection and Covid-19 severity
detection, respectively.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning