Convolutional Neural Networks (CNN) can achieve excellent computer-assisted diagnosis with a good amount of data. However, there is still a growing demand for specific data and information for training Machine Learning models, either for classification or other tasks such as segmentation. Towards this, the Data Augmentation (DA) technique can handle the small medical imaging dataset problem by generating artificial training data. In this context, Generative Adversarial Networks (GANs) can synthesize realistic images to increase the number of images in a dataset. Therefore, to maximize the DA efficiency in a CNNbased tumor classification task, we propose using non-extensive Gabor filters as a convolutional layer kernel initializer. Our proposal has been tested in the BraTS15 dataset and results show that CNN with an additional q-Gabor layer can achieve an average accuracy 3.65% better than CNN with Gabor and 5.03% better than the default model when trained with artificial images (data augmentation).

Investments in Augmented Reality (AR) have grown considerably in recent years. This advance is due to the increased use of AR in areas such as education, training, games and medicine. In addition, technological advances in hardware enable devices that, a few years ago, were unthinkable. A popular example is Microsoft Hololens 2, which allows the user to use their own hands as a means of interacting with an AR experience. However, a disadvantage from this device is its high cost due to several sensors. Thus, this project offers an AR architecture that uses only a monocular RGB camera as a sensor, allowing the user to interact with an AR experience using their hands to perform gestures similar to the Microsoft Hololens 2 architecture, where it is possible to handle a virtual object in the same way that a real object would be manipulated. The results obtained are promising, where the verification of the interaction of the hand with the virtual object worked in approximately 80% of the tests carried out, respecting the path defined by hand movement.