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
,,
WANG Xiang¹,
YUE Zhen²,
YU Xiaofan^{1
,
,}

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 210006, China

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

Received Date: 2025-09-02
Accepted Date: 2026-01-22
Rev Recd Date: 2026-01-22

Available Online: 2026-02-11

Abstract

Abstract

Objective Convolutional Neural Network (CNN)-based Computer-Aided Diagnosis (CAD) systems have advanced brain tumor classification in recent years. However, performance remains limited by feature confusion and insufficient modeling of high-order interactions. This study proposes a framework that integrates causal feature guidance with a KAN attention mechanism. A Confusion Balance Index (CBI) is developed to quantify real label distribution within clusters. A causal intervention mechanism then incorporates confused samples to strengthen discrimination between causal variables and confounding factors. A spline-based KAN attention module is further constructed to model high-order feature interactions and enhance focus on critical lesion regions and discriminative features. The combined causal modeling and nonlinear interaction enhancement improves robustness and addresses the inability of traditional architectures to capture complex pathological feature relationships. Methods A pre-trained CLIP model is used for feature extraction to obtain semantically rich visual representations. K-means clustering and the CBI are applied to identify confusing factor images, after which a causal intervention mechanism incorporates these samples into the training process. A causal-enhanced loss function is then designed to strengthen discrimination between causal variables and confounding factors. To address limited high-order interaction modeling, a Kolmogorov-Arnold Network (KAN)-based attention mechanism is integrated. This spline-based module constructs flexible nonlinear attention representations and refines high-order feature interactions. When fused with the backbone network, it improves discriminative performance and generalization. Results and Discussions The proposed method achieves superior performance across three datasets. On DS1, the model reaches 99.92% accuracy, 99.98% specificity, and 99.92% precision, outperforming RanMerFormer (+0.15%) and SAlexNet (+0.23%) and exceeding traditional CNNs by more than 2% (95%～97%). Swin Transformers reach 98.08% accuracy but only 91.75% precision, indicating stronger robustness of the proposed model in reducing false detections. On DS2, the method achieves 98.86% accuracy and 98.80% precision, exceeding the next-best RanMerFormer. On a more challenging in-house dataset, it maintains 90.91% accuracy and 95.45% specificity, showing generalization in complex settings. The gains result from the KAN attention mechanism’s ability to model high-order interactions and the causal reasoning module’s decoupling of confounding factors. These components improve focus on lesion regions and stabilize decision-making in complex scenarios. The results demonstrate reliable performance for clinical precision diagnostics. Conclusions The findings confirm that the proposed framework improves brain tumor classification. The combined effect of the causal intervention mechanism and the KAN attention module is the primary contributor to performance gains. These improvements require minimal increases in model parameters and inference latency, preserving efficiency and practicality. The study proposes a methodological direction for medical image classification and shows potential utility in few-shot learning and clinical decision support.
- Medical image processing,
- Brain tumor classification,
- Causal Inference,
- Attention mechanism

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References(44)

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