Researchers have demonstrated that an attention-gated multi-task deep-learning model can jointly classify and segment breast ultrasound lesions with performance statistically comparable to dedicated single-task networks while cutting parameter requirements by 46 percent. On a held-out test set of 117 images from the publicly available BUSI dataset, the unified model achieved a classification accuracy of 93.16 percent, a macro F1-score of 0.931, and a macro area under the receiver operating characteristic curve of 0.984, statistically comparable to a dedicated classification-only baseline that recorded an accuracy of 91.45 percent (paired McNemar test p-value of 0.63). For lesion segmentation, the unified architecture achieved a mean Dice similarity coefficient of 0.766, with a bootstrap 95 percent confidence interval spanning 0.710 to 0.819 across 1,000 resamples, statistically comparable to the dedicated segmentation-only baseline's Dice score of 0.810 (paired Wilcoxon signed-rank test p = 0.22). Incorporating attention gates into the segmentation-only baseline resulted in a Dice score of 0.813 versus 0.810 (p = 0.50). The unified network demonstrated 100 percent recall for normal scans and misclassified no malignant cases as normal. Built upon a shared ResNet-34 encoder, the framework simultaneously executes three-class classification across normal, benign, and malignant cases alongside lesion segmentation within a single forward pass. The researchers developed and evaluated the architecture using the