@Article{cmc.2025.059807,
AUTHOR = {Zhiyong Li, Xinlian Zhou},
TITLE = {A Global-Local Parallel Dual-Branch Deep Learning Model with Attention-Enhanced Feature Fusion for Brain Tumor MRI Classification},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {83},
YEAR = {2025},
NUMBER = {1},
PAGES = {739--760},
URL = {http://www.techscience.com/cmc/v83n1/60079},
ISSN = {1546-2226},
ABSTRACT = {Brain tumor classification is crucial for personalized treatment planning. Although deep learning-based Artificial Intelligence (AI) models can automatically analyze tumor images, fine details of small tumor regions may be overlooked during global feature extraction. Therefore, we propose a brain tumor Magnetic Resonance Imaging (MRI) classification model based on a global-local parallel dual-branch structure. The global branch employs ResNet50 with a Multi-Head Self-Attention (MHSA) to capture global contextual information from whole brain images, while the local branch utilizes VGG16 to extract fine-grained features from segmented brain tumor regions. The features from both branches are processed through designed attention-enhanced feature fusion module to filter and integrate important features. Additionally, to address sample imbalance in the dataset, we introduce a category attention block to improve the recognition of minority classes. Experimental results indicate that our method achieved a classification accuracy of  and a micro-average Area Under the Curve (AUC) of 0.989 in the classification of three types of brain tumors, surpassing several existing pre-trained Convolutional Neural Network (CNN) models. Additionally, feature interpretability analysis validated the effectiveness of the proposed model. This suggests that the method holds significant potential for brain tumor image classification.},
DOI = {10.32604/cmc.2025.059807}
}