Open AccessA Methodology for Tumor Detection in MRI using a New q-Gabor Function as a Convolutional Filter
Author(s) -
Vinicius de A. Silva,
Lucas P. Laheras,
Éverton C. Acchetta,
Paulo S. Rodrigues
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/wvc.2021.18908
Subject(s) - computer science , artificial intelligence , convolutional neural network , pattern recognition (psychology) , gabor filter , kernel (algebra) , segmentation , context (archaeology) , image segmentation , deep learning , machine learning , image (mathematics) , paleontology , mathematics , combinatorics , biology
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).