AUTHOR=Chen Wenna , Tan Xinghua , Zhang Jincan , Du Ganqin , Fu Qizhi , Jiang Hongwei 

TITLE=MLG: a mixed local and global model for brain tumor classification

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 19 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1618514

DOI=10.3389/fnins.2025.1618514

ISSN=1662-453X

ABSTRACT=IntroductionBrain tumors seriously endanger human health. Therefore, accurately identifying the types of brain tumors and adopting corresponding treatment methods is of vital importance, which is of great significance for saving patients’ lives. The use of computer-aided systems (CAD) for the differentiation of brain tumors has proved to be a reliable scheme.MethodsIn this study, a highly accurate Mixed Local and Global (MLG) model for brain tumor classification is proposed. Compared to prior approaches, the MLG model achieves effective integration of local and global features by employing a gated attention mechanism. The MLG model employs Convolutional Neural Networks (CNNs) to extract local features from images and utilizes the Transformer to capture global characteristics. This comprehensive scheme renders the MLG model highly proficient in the task of brain tumor classification. Specifically, the MLG model is primarily composed of the REMA Block and the Biformer Block, which are fused through a gated attention mechanism. The REMA Block serves to extract local features, effectively preventing information loss and enhancing feature expressiveness. Conversely, the Biformer Block is responsible for extracting global features, adaptively focusing on relevant sets of key tokens based on query positions, thereby minimizing attention to irrelevant information and further boosting model performance. The integration of features extracted by the REMA Block and the Biformer Block through the gated attention mechanism further enhances the representation ability of the features.ResultsTo validate the performance of the MLG model, two publicly available datasets, namely the Chen and Kaggle datasets, were utilized for testing. Experimental results revealed that the MLG model achieved accuracies of 99.02% and 97.24% on the Chen and Kaggle datasets, respectively, surpassing other state-of-the-art models. This result fully demonstrates the effectiveness and superiority of the MLG model in the task of brain tumor classification.