A Novel Hybrid Deep Learning and Chaotic Dynamics Approach for Thyroid Cancer Classification

2025-09-28

AI Summary

To address the growing demand for precise diagnosis amid rising thyroid cancer incidence, this work proposes an intelligent ultrasound image classification method integrating chaotic dynamics with deep learning. Specifically, an *n*-scroll chaotic system modulates the detail coefficients of the CDF9/7 wavelet transform to enhance discriminative texture feature representation; an adaptive CNN architecture is designed, augmented with Grad-CAM and SHAP for model interpretability. Evaluated on the DDTI dataset, the method achieves 98.17% accuracy and an AUC of 0.9912—significantly outperforming state-of-the-art models such as EfficientNetV2-S—while maintaining efficient inference. Cross-dataset validation confirms strong generalization and robustness. This study pioneers the incorporation of chaotic modulation into a wavelet–deep learning framework, establishing a novel paradigm for AI-assisted medical image diagnosis.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Multi-modal Vision
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

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.

Problem

• Developing hybrid deep learning for thyroid cancer classification
• Enhancing ultrasound image features using chaotic modulation
• Achieving high accuracy with explainable AI for clinical use

Innovation

• Hybrid deep learning with chaotic dynamics for classification
• Adaptive CNN combined with CDF9/7 wavelets modulation
• Chaotic system enriches discriminative features in ultrasound