  In the field of medical imaging, breast ultrasound has emerged as a crucial
diagnostic tool for early detection of breast cancer. However, the accuracy of
diagnosing the location of the affected area and the extent of the disease
depends on the experience of the physician. In this paper, we propose a novel
model called UGGNet, combining the power of the U-Net and VGG architectures to
enhance the performance of breast ultrasound image analysis. The U-Net
component of the model helps accurately segment the lesions, while the VGG
component utilizes deep convolutional layers to extract features. The fusion of
these two architectures in UGGNet aims to optimize both segmentation and
feature representation, providing a comprehensive solution for accurate
diagnosis in breast ultrasound images. Experimental results have demonstrated
that the UGGNet model achieves a notable accuracy of 78.2% on the "Breast
Ultrasound Images Dataset."
