Adaptive dual-window enhancement and multi-scale texture prior fusion for robust kidney CT classification
Ping Xia, Yilin Li, Xin Yao, Yunjia Jiang, WeiMing He, Ming-gang Wei
TL;DR
This paper introduces a new framework for kidney CT classification that improves accuracy and robustness by combining adaptive image enhancement and multi-scale texture analysis.
Contribution
The novel framework combines ADWE and MTPF for enhanced contrast and texture modeling in kidney CT classification.
Findings
The method achieves 0.9802 accuracy and 0.9989 AUC in binary classification, outperforming existing models.
It improves four-class classification accuracy by 3%–5% over the ConvNeXtV2 baseline.
The method remains robust under noise, maintaining 0.8510 accuracy and 0.9634 AUC at σ=0.1.
Abstract
Accurate classification of kidney diseases is of great importance for clinical diagnosis and treatment. However, traditional CT images suffer from insufficient contrast, blurred tissue boundaries, and complex texture variations, which limit the performance of automated analysis. This paper proposes a novel kidney CT classification framework that combines Adaptive Dual-Window Enhancement (ADWE) with Multi-Scale Texture Prior Fusion (MTPF). The ADWE module dynamically adjusts window width and window level to generate complementary views, effectively enhancing the contrast of both soft tissues and high-density structures; the MTPF module incorporates edge, local binary pattern (LBP), and Gabor texture priors to achieve fine-grained structural modeling. Experimental results demonstrate that in the binary classification task, the proposed method achieves an accuracy of 0.9802, F1-score of…
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Taxonomy
TopicsAI in cancer detection · Advanced Neural Network Applications · Advanced X-ray and CT Imaging