Global-local feature fusion: a robust hybrid deep learning model for multiclass brain tumor classification with Grad-CAM++ interpretation.

Background: Accurate classification of brain tumors in MRI scans is critical for effective clinical decision making, yet manual assessment is labor-intensive and subject to variability. Although deep learning models, particularly CNNs and Transformers, have shown potential, each architecture alone has limitations: CNNs excel at local feature extraction but lack global context awareness, while Transformers capture global relationships but may overlook fine-grained details. Objectives: To develop a hybrid Transfer Learning-Transformer model that integrates CNN-driven local feature modeling with Transformer-based global reasoning to enhance multi-class brain tumor classification. Methods: The proposed workflow comprises three stages: (1) benchmarking standalone CNN (ResNet50, VGG19, ConvNeXtBase, EfficientNetV2B0) and Transformer models (ViT, Swin, DeiT, PoolFormer); (2) constructing two intra-family hybrids-H Results: The H Conclusion: The proposed hybrid model delivers high accuracy, strong generalizability, and reliable, interpretable predictions across MRI datasets, positioning it as a promising solution for AI-assisted brain tumor diagnosis.