An Improved U-Net Model for Ultrasound Image Segmentation of Breast Cancer

Breast cancer is the most common and one of the most lethal malignant tumors among women worldwide. Early and accurate diagnosis plays a crucial role in improving patient survival rates. As one of the primary imaging modalities, breast ultrasound imaging has been widely employed in clinical screening due to its low cost and lack of radiation exposure. However, limited by its imaging mechanism, ultrasound images often suffer from severe speckle noise interference, blurred boundaries, and complex tissue structures, which significantly hinder the performance of automatic lesion segmentation. To address this challenge, this paper proposes an improved Attention U-Net model. By introducing Attention Gate modules into the conventional U-Net architecture, the model is guided to focus on salient regions associated with lesions while suppressing background interference. Moreover, the network depth is increased to enhance feature representation capabilities. As a result, the proposed model achieves improved segmentation accuracy and boundary fitting performance in complex scenarios.