  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
