DB-FGA-Net: Dual Backbone Frequency Gated Attention Network for Multi-Class Brain Tumor Classification with Grad-CAM Interpretability

TL;DR

This paper introduces DB-FGA-Net, a robust, augmentation-free deep learning model combining dual backbones and attention mechanisms for accurate, interpretable brain tumor classification with clinical visualization tools.

Contribution

The novel integration of VGG16, Xception, and Frequency-Gated Attention blocks enables high accuracy without data augmentation, enhancing robustness and interpretability in brain tumor diagnosis.

Findings

Achieved 99.24% accuracy on 7K-DS dataset for 4-class classification.

Outperformed baseline methods on independent 3K-DS dataset with 95.77% accuracy.

Integrated Grad-CAM for tumor localization, improving clinical interpretability.

Abstract

Brain tumors are a challenging problem in neuro-oncology, where early and precise diagnosis is important for successful treatment. Deep learning-based brain tumor classification methods often rely on heavy data augmentation which can limit generalization and trust in clinical applications. In this paper, we propose a double-backbone network integrating VGG16 and Xception with a Frequency-Gated Attention (FGA) Block to capture complementary local and global features. Our model achieves highly competitive performance without augmentation which demonstrates robustness to variably sized and distributed datasets. For further transparency, Grad-CAM is integrated to visualize the tumor regions based on which the model is giving prediction, bridging the gap between model prediction and clinical interpretability. The proposed framework achieves 99.24% accuracy on the 7K-DS dataset for the 4-class setting, along with 98.68% and 99.85% in the 3-class and 2-class settings, respectively. On the independent 3K-DS dataset, the model generalizes with 95.77% accuracy, outperforming several baseline methods under the same experimental setting. To further support clinical usability, we developed a graphical user interface (GUI) that provides real-time classification and Grad-CAM-based tumor localization. These findings suggest that augmentation-free, interpretable, and deployable deep learning models such as DB-FGA-Net hold strong potential for reliable clinical translation in brain tumor diagnosis.