  With the COVID-19 global pandemic, computer-assisted diagnoses of medical
images have gained much attention, and robust methods of Semantic Segmentation
of Computed Tomography (CT) became highly desirable. Semantic Segmentation of
CT is one of many research fields of automatic detection of COVID-19 and has
been widely explored since the COVID-19 outbreak. In this work, we propose an
extensive analysis of how different data augmentation techniques improve the
training of encoder-decoder neural networks on this problem. Twenty different
data augmentation techniques were evaluated on five different datasets. Each
dataset was validated through a five-fold cross-validation strategy, thus
resulting in over 3,000 experiments. Our findings show that spatial level
transformations are the most promising to improve the learning of neural
networks on this problem.
