  To address the challenges of similarity between lesions and surrounding
tissues, overlapping appearances of partially benign and malignant nodules, and
difficulty in classification, a deep learning network that integrates CNN and
Transformer is proposed for the classification of benign and malignant breast
lesions in ultrasound images. This network adopts a dual-branch architecture
for local-global feature extraction, making full use of the advantages of CNN
in extracting local features and the ability of ViT to extract global features
to enhance the network's feature extraction capabilities for breast nodules.
The local feature extraction branch employs a residual network with multiple
attention-guided modules, which can effectively capture the local details and
texture features of breast nodules, enhance sensitivity to subtle changes
within the nodules, and thus can aid in accurate classification of their benign
and malignancy. The global feature extraction branch utilizes the multi-head
self-attention ViT network, which can capture the overall shape, boundary, and
relationship with surrounding tissues, and thereby enhancing the understanding
and modeling of both nodule and global image features. Experimental results on
a public ultrasound breast nodule data set show that the proposed method is
better than other comparison networks, This indicates that the fusion of CNN
and Transformer networks can effectively improve the performance of the
classification model and provide a powerful solution for the benign-malignant
classification of ultrasound breast.
