In developing the disease COVID-19, various means for diagnosis have been widely studied, so we arrive at medical imaging tests, where the chest tomography exam has the best level of detail among other imaging exams. However, the analysis of these images generates a repeatable and tiring workload, requiring a team of specialists familiar with the indicative findings of pneumonia caused by COVID-19. To reduce this manual work and collaborate with the specialists, this chapter presents pre-processing methods and techniques for classification and segmentation of these images, reaching 99% accuracy for classification and 87% Dice for segmentation. All this to collaborate with a theoretical and practical basis for future works.