用可解释深度迁移学习提升超声乳腺癌检测准确率
Interpretable Deep Transfer Learning for Breast Ultrasound Cancer Detection: A Multi-Dataset Study
- 基于ResNet-18的深度迁移学习模型，结合多数据集训练
- 达99.7%准确率，恶性病灶敏感性完美（100%）
- 通过Grad-CAM实现诊断区域可视化，适合临床部署
乳腺癌是全球女性癌症致死的主要原因。超声成像因其安全性和低成本，广泛用于早期检测，尤其对致密型乳腺组织患者。本文系统研究了机器学习与深度学习在乳腺超声图像分类中的应用。使用BUSI、BUS-BRA和BrEaST-Lesions USG等多个数据集，对比评估了SVM、KNN等经典机器学习模型以及ResNet-18、EfficientNet-B0、GoogLeNet等深度卷积神经网络。实验表明，ResNet-18达到最高准确率99.7%，并实现恶性病变100%敏感性。经典机器学习模型经深度特征提取后仍具竞争力。通过Grad-CAM可视化，模型能精准定位诊断相关区域，显著提升可解释性。研究支持将AI辅助诊断工具融入临床流程，验证了高性能、可解释系统在超声乳腺癌检测中的可行性。
原文摘要 · Abstract (English)
Breast cancer remains a leading cause of cancer-related mortality among women worldwide. Ultrasound imaging, widely used due to its safety and cost-effectiveness, plays a key role in early detection, especially in patients with dense breast tissue. This paper presents a comprehensive study on the application of machine learning and deep learning techniques for breast cancer classification using ultrasound images. Using datasets such as BUSI, BUS-BRA, and BrEaST-Lesions USG, we evaluate classical machine learning models (SVM, KNN) and deep convolutional neural networks (ResNet-18, EfficientNet-B0, GoogLeNet). Experimental results show that ResNet-18 achieves the highest accuracy (99.7%) and perfect sensitivity for malignant lesions. Classical ML models, though outperformed by CNNs, achieve competitive performance when enhanced with deep feature extraction. Grad-CAM visualizations further improve model transparency by highlighting diagnostically relevant image regions. These findings support the integration of AI-based diagnostic tools into clinical workflows and demonstrate the feasibility of deploying high-performing, interpretable systems for ultrasound-based breast cancer detection.
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