In this research, different models of Convolutional Neural Networks were created, varying the filter parameters and times in order to improve learning about the different classes of the set of images. Likewise, the set of images was subdivided into training, test and validation images, the latter to verify the efficiency in the classification of the created model which presented an efficiency of 87.5% and which is close to the best found in the literature.
