A Causality-Guided KAN Attention Framework for Brain Tumor Classification

FAN Yawen; WANG Xiang; YUE Zhen; YU Xiaofan

doi:10.11999/JEIT250865

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FAN Yawen, WANG Xiang, YUE Zhen, YU Xiaofan. A Causality-Guided KAN Attention Framework for Brain Tumor Classification[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250865

Citation:

FAN Yawen, WANG Xiang, YUE Zhen, YU Xiaofan. A Causality-Guided KAN Attention Framework for Brain Tumor Classification[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250865

FAN Yawen, WANG Xiang, YUE Zhen, YU Xiaofan. A Causality-Guided KAN Attention Framework for Brain Tumor Classification[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250865

Citation:

FAN Yawen, WANG Xiang, YUE Zhen, YU Xiaofan. A Causality-Guided KAN Attention Framework for Brain Tumor Classification[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250865

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A Causality-Guided KAN Attention Framework for Brain Tumor Classification

doi: 10.11999/JEIT250865 cstr: 32379.14.JEIT250865

FAN Yawen^{1, 2},
WANG Xiang^{1, 2},
YUE Zhen^{1, 2},
YU Xiaofan^{1, 2
,
,}

1.
National Engineering Research Center of Communications and Networking, Nanjing University of Posts Telecommunications, Nanjing 210003, China
2.
Department of Neurosurgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China

Funds: The National Natural Science Foundation of China(62476139)

Accepted Date: 2026-01-22
Rev Recd Date: 2026-01-22

Available Online: 2026-02-11

Abstract

Abstract

Objective In recent years, Convolutional Neural Network (CNN)-based Computer-Aided Diagnosis (CAD) systems have advanced brain tumor classification. However, classification performance remains limited due to feature confusion and inadequate modeling of high-order interactions. To address these challenges, this study proposes an innovative framework that integrates causal feature guidance with a KAN attention mechanism. A novel metric, the Confusion Balance Index (CBI), is introduced to quantify real label distribution within clusters. Furthermore, a causal intervention mechanism is designed to explicitly incorporate confused samples, enhancing the model’s ability to distinguish causal variables from confounding factors. In addition, a KAN attention module based on spline functions is constructed to accurately model high-order feature interactions, thereby strengthening the model’s focus on critical lesion regions and discriminative features. This dual-path optimization approach, combining causal modeling with nonlinear interaction enhancement, improves classification robustness and overcomes the limitations of traditional architectures in capturing complex pathological feature relationships. Methods This study employs a pre-trained CLIP model for feature extraction, leveraging its representation capabilities to obtain semantically rich visual features. Subsequently, based on K-means clustering, the Confusion Balance Index (CBI) is introduced to identify confusing factor images, and a causal intervention mechanism is implemented to explicitly incorporate these confused samples into the training set. Based on this, a causal-enhanced loss function is designed to optimize the model’s ability to discriminate causal variables from confounding factors. Furthermore, to address insufficient high-order feature modeling, a Kolmogorov-Arnold Network (KAN)-based attention mechanism is further integrated. This module, built on spline functions, constructs flexible nonlinear attention representations to finely model high-order feature interactions. By fusing this module with the backbone network, the model achieves enhanced discriminative performance and generalization capabilities. Results and Discussions The proposed method significantly outperforms existing approaches across three datasets. On DS1, the model achieves 99.92% accuracy, 99.98% specificity, and 99.92% precision, surpassing benchmarks such as RanMerFormer (+0.15%) and SAlexNet (+0.23%), with improvements exceeding 2% over traditional CNN methods (95%–97%). Although Swin Transformers achieve 98.08% accuracy, their precision (91.75%) highlights the superior robustness of our method in reducing false detections. On DS2, the model attains 98.86% accuracy and 98.80% precision, outperforming the second-best RanMerFormer. On a more challenging in-house dataset, the model maintains 90.91% accuracy and 95.45% specificity, demonstrating generalization to complex scenarios. Performance gains are attributed to the KAN attention mechanism's high-order feature interaction modeling and the causal reasoning module's confounding factor decoupling. These components enhance focus on critical lesion regions and improve decision-making stability in complex scenarios. Experimental results validate the framework's superiority in brain tumor classification, offering reliable support for clinical precision diagnostics. Conclusions Experimental results substantiate that the proposed framework confers marked improvements in brain tumor classification, with the synergistic interaction between the causal intervention mechanism and the KAN attention module serving as the principal factor driving performance gains. Notably, these enhancements are achieved with negligible increases in model parameters and inference latency, thereby ensuring both efficiency and practicality. This study delineates a novel methodological paradigm for medical image classification and underscores its prospective utility in few-shot learning scenarios and clinical decision support systems.
- Medical image processing,
- Brain tumor classification,
- Causal Inference,
- Attention mechanism

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References(44)

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