  COVID-19 presence classification and severity prediction via (3D) thorax
computed tomography scans have become important tasks in recent times.
Especially for capacity planning of intensive care units, predicting the future
severity of a COVID-19 patient is crucial. The presented approach follows
state-of-theart techniques to aid medical professionals in these situations. It
comprises an ensemble learning strategy via 5-fold cross-validation that
includes transfer learning and combines pre-trained 3D-versions of ResNet34 and
DenseNet121 for COVID19 classification and severity prediction respectively.
Further, domain-specific preprocessing was applied to optimize model
performance. In addition, medical information like the infection-lung-ratio,
patient age, and sex were included. The presented model achieves an AUC of
79.0% to predict COVID-19 severity, and 83.7% AUC to classify the presence of
an infection, which is comparable with other currently popular methods. This
approach is implemented using the AUCMEDI framework and relies on well-known
network architectures to ensure robustness and reproducibility.
