This study aimed to improve breast tumor segmentation in ultrasound images using a modified U‑Net architecture that reduces trainable parameters while enhancing segmentation accuracy and diagnostic performance. A lightweight modified U‑Net with fewer trainable parameters was developed for breast ultrasound tumor segmentation using the BUSI and UDAIT datasets, and to compensate for the simplified structure and reduce overfitting risk, the input channels were diversified using complementary tumor and non‑tumor region enhancement information, which improved both direct and indirect segmentation metrics while maintaining lower computational complexity than recent U‑Net‑based models. The model performance was evaluated using accuracy, precision, sensitivity, specificity, F1‑score, Dice coefficient, and Intersection over Union (IoU). On the BUSI dataset, the proposed method achieved an accuracy of 0.99, precision of 0.95, sensitivity of 0.96, specificity of 0.99, F1‑score of 0.95, Dice coefficient of 0.95, and IoU of 0.92; on the UDAIT dataset, it obtained an accuracy of 0.98, precision of 0.88, sensitivity of 0.96, specificity of 0.98, F1‑score of 0.92, Dice coefficient of 0.92, and IoU of 0.85. Compared with previous approaches, the proposed method achieved superior segmentation performance, particularly in Dice coefficient and F1‑score, while maintaining lower computational complexity due to fewer trainable parameters. In conclusion, the proposed modified U‑Net model improved breast tumor segmentation in ultrasound imaging while maintaining lower computational complexity than newer U‑Net variants, and its high accuracy and Dice scores indicate its potential as an efficient decision‑support tool for breast cancer diagnosis.