脑癌被广泛认为是最具侵略性的肿瘤之一。事实上，约70%的被诊断患有这种恶性癌症的患者无法幸存。本文提出了一种方法，旨在从磁共振图像的分析开始，检测和定位脑癌。所提出的方法利用深度学习，尤其是卷积神经网络和类激活映射，以便通过突出显示与脑癌相关的医学图像区域（从模型观点）提供可解释性。我们使用一个免费可用的数据集对所提出的方法进行了3000次磁共振评估。我们获得了令人鼓舞的结果。通过利用四种不同的模型：VGG16、ResNet50、Alex_Net和MobileNet，我们在脑癌检测方面达到了从97.83%到99.67%的准确率，从而展现了所提出方法的有效性。

Brain cancer is widely recognised as one of the most aggressive types of tumors. In fact, approximately 70% of patients diagnosed with this malignant cancer do not survive. In this paper, we propose a method aimed to 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, in order 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.