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In the field of medical imaging, breast ultrasound has emerged as a crucialdiagnostic tool for early detection of breast cancer. However, the accuracy ofdiagnosing the location of the affected area and the extent of the diseasedepends on the experience of the physician. In this paper, we propose a novelmodel called UGGNet, combining the power of the U-Net and VGG architectures toenhance the performance of breast ultrasound image analysis. The U-Netcomponent of the model helps accurately segment the lesions, while the VGGcomponent utilizes deep convolutional layers to extract features. The fusion ofthese two architectures in UGGNet aims to optimize both segmentation andfeature representation, providing a comprehensive solution for accuratediagnosis in breast ultrasound images. Experimental results have demonstratedthat the UGGNet model achieves a notable accuracy of 78.2% on the "BreastUltrasound Images Dataset."

UGGNet: Bridging U-Net and VGG for Advanced Breast Cancer Diagnosis