Explainable Deep Learning for Brain Cancer Detection and Localisation.

Brain cancer is considered one of the most ag-gressive tumors, as a matter of fact, the 70% of the patients diagnosed with this malignant cancer will not survive. In this paper, we propose a method aimed to automatically detect and localise brain cancer, starting from the analysis of magnetic resonance images. The proposed method exploits deep learning, in particular convolutional neural networks, and class activation mapping to provide explainability by highlighting the areas of the medical image related to brain cancer (from the model point of view). We evaluate the proposed method with 3000 magnetic resonances using a free available dataset. The results we obtained are encouraging: we reach an accuracy ranging from 97.83% to 99.67% in brain cancer detection by exploiting four different models: VGG16, ResNet50, Alex_Net and MobileNet, thus showing the effectiveness of the proposed method.