Deep learning methods have become a powerful tool in medical imaging, with great potential to improve diagnostic accuracy and support early disease detection. This is especially crucial for breast cancer, one of the most common cancers among women, where early detection of abnormal tissue is key to improving survival rates. AI-based methods show great promise in detecting this pathology. In this study, we explore the application of deep learning techniques to classify breast masses as malignant or benign using ultrasound images, aiming to support breast cancer diagnosis. We propose a workflow that integrates two neural networks: a U-Net for image segmentation and a SegNet for classification. An ablation study was conducted to determine the optimal configuration of parameters. Our approach was tested on 780 ultrasound images. The results show promising improvements in diagnostic accuracy, demonstrating the potential of AI-powered tools to significantly enhance the early detection of breast cancer.