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

• URL: http://arxiv.org/abs/2510.20299v2
• Date: Sat, 25 Oct 2025 01:40:13 GMT
• Title: DB-FGA-Net: Dual Backbone Frequency Gated Attention Network for Multi-Class Brain Tumor Classification with Grad-CAM Interpretability
• Authors: Saraf Anzum Shreya, MD. Abu Ismail Siddique, Sharaf Tasnim,
• Abstract summary: We propose a double-backbone network integrating VGG16 and Xception with a Frequency-Gated Attention (FGA) Block to capture complementary local and global features.<n>Our model achieves state-of-the-art performance without augmentation which demonstrates robustness to variably sized and distributed datasets.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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. Unlike previous studies, our model achieves state-of-the-art 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 baseline and state-of-the-art methods. 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.

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