A Methodology for Tumor Detection in MRI using a New q-Gabor Function as a Convolutional Filter

Abstract

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).