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With the COVID-19 global pandemic, computer-assisted diagnoses of medicalimages have gained much attention, and robust methods of Semantic Segmentationof Computed Tomography (CT) became highly desirable. Semantic Segmentation ofCT is one of many research fields of automatic detection of COVID-19 and hasbeen widely explored since the COVID-19 outbreak. In this work, we propose anextensive analysis of how different data augmentation techniques improve thetraining of encoder-decoder neural networks on this problem. Twenty differentdata augmentation techniques were evaluated on five different datasets. Eachdataset was validated through a five-fold cross-validation strategy, thusresulting in over 3,000 experiments. Our findings show that spatial leveltransformations are the most promising to improve the learning of neuralnetworks on this problem.

Light In The Black: An Evaluation of Data Augmentation Techniques for COVID-19 CT's Semantic Segmentation