Abstract
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.
Abstract (translated)
乳腺癌仍然是全球女性癌症死亡的主要原因之一。超声成像由于其安全性及成本效益，被广泛用于早期检测，特别是在具有致密乳腺组织的患者中发挥关键作用。本文介绍了一项关于使用机器学习和深度学习技术对乳腺癌进行分类的综合研究，通过使用包括BUSI、BUS-BRA和BrEaST-Lesions USG等数据集，评估了传统机器学习模型（如支持向量机SVM和K近邻算法KNN）以及深度卷积神经网络（ResNet-18、EfficientNet-B0、GoogLeNet）。实验结果表明，ResNet-18取得了最高的准确率（99.7%），并且对恶性肿瘤的敏感性达到了完美。尽管传统的机器学习模型在性能上被CNNs超越，但通过深度特征提取增强后，它们仍能获得有竞争力的表现。Grad-CAM可视化技术进一步提升了模型的透明度，通过突出显示具有诊断相关性的图像区域来提高解释能力。这些发现支持将基于AI的诊断工具整合到临床工作流程中，并展示了部署高性能且易于解释的系统用于超声波乳腺癌检测的可行性。
URL
https://arxiv.org/abs/2509.05004
