The breast cancer classification task is a widely tackled problem in deep learning, since the computational power of computers has been allowing the analysis of medical images for several years now. Although new types of neural network architectures, such as Visual Transformers and Anatomy aware Graph convolutional Networks, have recently emerged and seem to be promising, the majority of the solutions proposed in the literature are still based on Convolutional Neural Networks, which are known to be efficient for computer vision applications. In order to compare the performances of the three types of architectures mentioned, three models, each based on one of the architectures, have been trained and evaluated on the same dataset, and the same classification tasks.