AN L2-Normalized Spatial Attention Network for Accurate and Fast Classification of Brain Tumors in 2D T1-Weighted CE-MRI Images

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, Gli...

Full description

Saved in:
Bibliographic Details
Published in2023 IEEE International Conference on Image Processing (ICIP) pp. 1895 - 1899
Main Authors Billingsley, Grace, Dietlmeier, Julia, Narayanaswamy, Vivek, Spanias, Andreas, O'Connor, Noel E.
Format Conference Proceeding
LanguageEnglish
Published IEEE 08.10.2023
Subjects
attention mechanisms
Brain modeling
brain tumor classification
Deep learning
Image processing
Magnetic resonance imaging
Medical services
MRI image analysis
Performance gain
Training
Transfer learning
Online AccessGet full text

Cover

More Information
Summary: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 l 2 -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 l 2 -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.
DOI:10.1109/ICIP49359.2023.10222887