  Amidst the ongoing pandemic, several studies have shown that COVID-19
classification and grading using computed tomography (CT) images can be
automated with convolutional neural networks (CNNs). Many of these studies
focused on reporting initial results of algorithms that were assembled from
commonly used components. The choice of these components was often pragmatic
rather than systematic. For instance, several studies used 2D CNNs even though
these might not be optimal for handling 3D CT volumes. This paper identifies a
variety of components that increase the performance of CNN-based algorithms for
COVID-19 grading from CT images. We investigated the effectiveness of using a
3D CNN instead of a 2D CNN, of using transfer learning to initialize the
network, of providing automatically computed lesion maps as additional network
input, and of predicting a continuous instead of a categorical output. A 3D CNN
with these components achieved an area under the ROC curve (AUC) of 0.934 on
our test set of 105 CT scans and an AUC of 0.923 on a publicly available set of
742 CT scans, a substantial improvement in comparison with a previously
published 2D CNN. An ablation study demonstrated that in addition to using a 3D
CNN instead of a 2D CNN transfer learning contributed the most and continuous
output contributed the least to improving the model performance.
