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Breast cancer detection through ultrasound imaging presents challenges due to variability in image quality and interpretation. This study introduces a novel ResNext-based U-Net architecture for the segmentation of sonomammograms, aiming to enhance accuracy and reliability. The proposed model integrates ResNext blocks within the U-Net framework, leveraging the residual connections to improve feature extraction and gradient propagation. We evaluated the model's performance across five-folds, comparing it with a baseline U-Net and with ResNet encoders. Our results indicate that the inclusion of ResNext blocks improves segmentation performance, particularly in capturing finer details and enhancing specificity. The enhanced architecture offers a promising tool for aiding radiologists in the early detection and diagnosis of breast cancer, providing a reliable and accurate method for automated sonomammogram segmentation.