Query-guided learning for efficient and interpretable multi-class brain tumor classification in MRI.

Overview

1. School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, India

Abstract

Introduction: Accurate and efficient multi-class classification for brain tumors from MRI images is still a significant problem, requiring not only local feature learning but also global context comprehension.

Methods: We present a novel Query-Guided Cross-Attention Convolutional Neural Network (QGC-CNN), which incorporates convolutional learning into a transformer-style network with learnable query tokens. The interaction between query tokens and feature maps helps discover long-range relationships that are difficult for standard CNNs to capture. To improve reliability and robustness, an ensemble method combining Xception, EfficientNetB0, ResNet50, and QGC-CNN was constructed, where the predicted values were combined using a LightGBM meta-learner.

Results: The experiments show that the proposed framework achieved a test accuracy of 95.50% and an AUC of 0.99 for the “No Tumor” class. Importantly, the QGC-CNN model shows great efficiency, having just 0.83 GFLOPs, which is 10.9 times lower than that of Xception.

Discussion: The proposed framework demonstrates that combining query-guided cross-attention with ensemble learning can provide accurate and computationally efficient brain tumor classification from MRI images.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• kaggle.com/datasets/ahmedhamada0, at Kaggle; found in the references
• kaggle.com/datasets/masoudnickparvar, at Kaggle; found in “Data availability statement”

Data availability statement

The data used in this study are publicly available from the Brain Tumor MRI Dataset by Nickparvar (2021) on Kaggle: https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset. All experiments and analyses reported in this study were conducted using this dataset.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 2, 28 September 2026

• Funding: added VIT University

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 2 authors, 8 keywords, 51 references.

Cite

This paper

Uthra, B., & Sivashankari, R. (2026). Query-guided learning for efficient and interpretable multi-class brain tumor classification in MRI. Frontiers in artificial intelligence, 9, 1849051. https://doi.org/10.3389/frai.2026.1849051

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