Author Identifier (ORCID)
Abstract
Accurate brain tumor classification via MRI is essential for diagnosis and treatment. This study introduces RViT-FusionNet, a hybrid deep learning model that integrates convolutional and transformer architectures for enhanced tumor detection. The model utilizes ResNet-50 to capture textural details and a Vision Transformer for extracting global context. A Local Cross-Attention (LCA) module is proposed to align and merge these features, allowing the network to model local structures and long-range dependencies concurrently. To enhance generalization across varied imaging conditions and tumor types, a domain discriminator is included to discern spatial and domain-specific patterns, fostering the learning of domain-invariant representations. The approach is validated on four public MRI datasets, yielding classification accuracies of 99.08% ± 0.16%, 99.56% ± 0.17%, 96.20% ± 0.25% and 94.76% ± 0.35% for glioma, meningioma, pituitary tumors, and healthy cases, respectively. For interpretability, Grad-CAM is utilized to create saliency maps highlighting tumor regions, confirming the model’s focus on clinically relevant areas. These findings demonstrate that RViT-FusionNet achieves exceptional performance while maintaining high interpretability, positioning it as an effective tool for computer-assisted brain tumor diagnosis. This framework is posited as a robust and scalable solution for multi-class brain tumor classification in clinical practice. The code is available at https://github.com/naimaislam-ice/RViT-FusionNet-LCA-based-brain-tumor-classification.
Keywords
Brain tumor classification, Grad-CAM, Local Cross-Attention, MRI, RViT-FusionNet
Document Type
Journal Article
Date of Publication
6-1-2026
Article Number
518
ISSN
09410643
Volume
38
Issue
12
Publication Title
Neural Computing and Applications
Publisher
Springer
School
School of Science
RAS ID
99646
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Islam, N., Ray, S. K., Hossain, M. A., & Islam, S. M. S. (2026). RViT-FusionNet: A local cross-attention feature fusion-based hybrid framework for brain tumor classification. Neural Computing and Applications, 38, Article 518. https://doi.org/10.1007/s00521-026-12290-x