Accurate classification of benign and malignant breast tumors in ultrasound imaging with an enhanced deep learning model

作者：Bao-Qin Liu, S. Liu, Zijian Cao, Junning Zhang, Pu Xia, Junjie Yu · 发表于：Frontiers in Bioengineering and Biotechnology · 年份：2025 · DOI：10.3389/fbioe.2025.1526260 · 被引用次数：3 · 研究领域：AI in cancer detection、Infrared Thermography in Medicine、Breast Lesions and Carcinomas

Background: Breast cancer is the most common malignant tumor in women worldwide, and early detection is crucial to improving patient prognosis. However, traditional ultrasound examinations rely heavily on physician judgment, and diagnostic results are easily influenced by individual experience, leading to frequent misdiagnosis or missed diagnosis. Therefore, there is a pressing need for an automated, highly accurate diagnostic method to support the detection and classification of breast cancer. This study aims to build a reliable breast ultrasound image benign and malignant classification model through deep learning technology to improve the accuracy and consistency of diagnosis. Methods: This study proposed an innovative deep learning model RcdNet. RcdNet combines deep separable convolution and Convolutional Block Attention Module (CBAM) attention modules to enhance the ability to identify key lesion areas in ultrasound images. The model was internally validated and externally independently tested, and compared with commonly used models such as ResNet, MobileNet, RegNet, ViT and ResNeXt to verify its performance advantage in benign and malignant classification tasks. In addition, the model's attention area was analyzed by heat map visualization to evaluate its clinical interpretability. Results: The experimental results show that RcdNet outperforms other mainstream deep learning models, including ResNet, MobileNet, and ResNeXt, across all key evaluation metrics. On the exter...