We propose an accurate and fast classification network for classification of brain tumors in MRI images that outperforms all lightweight methods investigated in terms of accuracy. We test our model on a challenging 2D T1-weighted CE-MRI dataset containing three types of brain tumors: Meningioma, Glioma and Pituitary. We introduce an l2-normalized spatial attention mechanism that acts as a regularizer against overfitting during training. We compare our results against the state-of-the-art on this dataset and show that by integrating l2-normalized spatial attention into a baseline network we achieve a performance gain of 1.79 percentage points. Even better accuracy can be attained by combining our model in an ensemble with the pretrained VGG16 at the expense of execution speed. Our code is publicly available at https://github.com/juliadietlmeier/MRI_image_classification

翻译：我们提出了一种用于MRI图像中脑肿瘤分类的准确快速分类网络，在精度上超过所有经过研究的轻量级方法。我们在一个包含三种脑肿瘤（脑膜瘤、胶质瘤和垂体瘤）的具有挑战性的二维T1加权增强MRI数据集上测试了模型。我们引入了一种L2归一化空间注意力机制，该机制在训练过程中作为防止过拟合的正则化器。我们将结果与在该数据集上的现有最优方法进行了比较，结果表明，通过将L2归一化空间注意力集成到基线网络中，我们实现了1.79个百分点的性能提升。通过将我们的模型与预训练的VGG16进行集成组合，可以在牺牲执行速度的情况下获得更高的精度。我们的代码已在https://github.com/juliadietlmeier/MRI_image_classification公开提供。