The Segmentation Model for Breast Cancer Ultrasound Image based on Attention U-Net

The segmentation of ultrasound image for breast cancer is an important task in the field of biomedical research. The traditional U-Net model, with its simple structure and remarkable performance, this approach has found extensive application in the segmentation of medical images. However, U-Net tends to be affected by background noise when handling images with complex backgrounds or blurry boundaries, which may impact the segmentation accuracy. To address this issue, the Attention U-Net model incorporates an attention mechanism, enabling the model to selectively focus on critical target areas within the image, thereby improving segmentation accuracy. This paper further optimizes the Attention U-Net architecture by increasing the depth of both the encoder and decoder sections, enhancing the model's capacity for feature extraction and image reconstruction. Consequently, both the accuracy and robustness of segmentation are enhanced. The experimental findings indicate that the proposed modified Attention U-Net model significantly outperforms traditional methods in breast ultrasound image segmentation tasks. It effectively handles various types of breast images, particularly those with complex backgrounds, blurred targets, or small sizes, maintaining high segmentation accuracy. This study offers an effective solution for the automated segmentation of breast ultrasound images, with substantial implications for enhancing both the automation and diagnostic efficiency in medical image analysis.