Open Access

ARTICLE

A Global-Local Parallel Dual-Branch Deep Learning Model with Attention-Enhanced Feature Fusion for Brain Tumor MRI Classification

Zhiyong Li, Xinlian Zhou^*

School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411100, China

* Corresponding Author: Xinlian Zhou. Email:

(This article belongs to the Special Issue: Medical Imaging Based Disease Diagnosis Using AI)

Computers, Materials & Continua 2025, 83(1), 739-760. https://doi.org/10.32604/cmc.2025.059807

Received 17 October 2024; Accepted 02 January 2025; Issue published 26 March 2025

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.

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