用混沌系统增强小波特征，提升甲状腺超声癌变分类准确率
A Novel Hybrid Deep Learning and Chaotic Dynamics Approach for Thyroid Cancer Classification
- 将混沌系统调制小波细节系数，强化CNN的判别特征
- 在DDTI数据集上达98.17%准确率，比纯小波CNN高8.79个百分点
- 兼具高精度、低延迟与可解释性，适合临床部署
及时准确的诊断对应对全球甲状腺癌上升趋势至关重要。本文提出一种智能分类方法，将自适应卷积神经网络（CNN）与Cohen-Daubechies-Feauveau（CDF9/7）小波结合，利用n-scroll混沌系统调制小波细节系数，以增强判别特征。在公开的DDTI甲状腺超声数据集（n = 1,638张图像；819例恶性，819例良性）上采用5折交叉验证，所提方法达到98.17%准确率、98.76%敏感度、97.58%特异度、97.55% F1分数和0.9912 AUC。消融实验显示，加入混沌调制使准确率相比仅使用CDF9/7的小波CNN（89.38%）提升8.79个百分点。与当前先进模型对比：EfficientNetV2-S（96.58%准确率；AUC 0.987）、Swin-T（96.41%；0.986）、ViT-B/16（95.72%；0.983）、ConvNeXt-T（96.94%；0.987），本方法在准确率上领先1.23个百分点，AUC领先0.0042，且计算效率高（单图推理28.7毫秒，峰值显存1,125 MB）。跨数据集测试在TCIA上达95.82%准确率，迁移至ISIC皮肤病变子集（28张原始图像，扩增至2,048张）获97.31%准确率。梯度类激活映射（Grad-CAM）、SHAP、LIME等可解释性分析揭示了临床相关区域。整体表明，该小波-混沌-CNN流程在甲状腺超声分类中达到顶尖性能，具备强泛化能力与实际部署可行性。
原文摘要 · Abstract (English)
Timely and accurate diagnosis is crucial in addressing the global rise in thyroid cancer, ensuring effective treatment strategies and improved patient outcomes. We present an intelligent classification method that couples an Adaptive Convolutional Neural Network (CNN) with Cohen-Daubechies-Feauveau (CDF9/7) wavelets whose detail coefficients are modulated by an n-scroll chaotic system to enrich discriminative features. We evaluate on the public DDTI thyroid ultrasound dataset (n = 1,638 images; 819 malignant / 819 benign) using 5-fold cross-validation, where the proposed method attains 98.17% accuracy, 98.76% sensitivity, 97.58% specificity, 97.55% F1-score, and an AUC of 0.9912. A controlled ablation shows that adding chaotic modulation to CDF9/7 improves accuracy by +8.79 percentage points over a CDF9/7-only CNN (from 89.38% to 98.17%). To objectively position our approach, we trained state-of-the-art backbones on the same data and splits: EfficientNetV2-S (96.58% accuracy; AUC 0.987), Swin-T (96.41%; 0.986), ViT-B/16 (95.72%; 0.983), and ConvNeXt-T (96.94%; 0.987). Our method outperforms the best of these by +1.23 points in accuracy and +0.0042 in AUC, while remaining computationally efficient (28.7 ms per image; 1,125 MB peak VRAM). Robustness is further supported by cross-dataset testing on TCIA (accuracy 95.82%) and transfer to an ISIC skin-lesion subset (n = 28 unique images, augmented to 2,048; accuracy 97.31%). Explainability analyses (Grad-CAM, SHAP, LIME) highlight clinically relevant regions. Altogether, the wavelet-chaos-CNN pipeline delivers state-of-the-art thyroid ultrasound classification with strong generalization and practical runtime characteristics suitable for clinical integration.
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