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Automated diagnosis of complex breast ultrasound images using deep learning has important clinical implications. In this study, we propose a novel breast cancer diagnosis framework, the CAS framework, based on lesion region to effectively recognize benign and malignant tumors. Firstly, train the segmentation network CRA-ENet on a public dataset with masks and save the trained weights. Then, the weights are used to segment clinical data without masks to obtain masks. Next, cover the masks on the corresponding original images to obtain ultrasound images that only include the lesion region. Finally, the SA-Net is employed to recognize benign and malignant tumors in images. Both the CRA-ENet and SA-Net are novel models explicitly designed for this study. The CRA-ENet is a network derived from ENet through pruning, convolutional replacement, and the incorporation of a hybrid attention mechanism, which can accurately segment breast tumor boundaries, allowing precise localization of lesion regions. The SA-Net is an innovative multi-branch parallel lightweight network that can enable effective artifact suppression and extraction of lesion-relevant features, significantly improving classification accuracy. In experimental, to validate the performance and efficiency of the proposed framework on clinical data provided by Yunnan Cancer Hospital based on the public dataset BUSI. Compared with simple classification utilizing only the classification network, the proposed approach improved accuracy, precision, and F1 score by 4.01%, 3.98%, and 4.00%, respectively, demonstrating the advantages of the framework. [ABSTRACT FROM AUTHOR]