DFMFI: ultrasound breast cancer detection method based on dynamic fusion multi-scale feature interaction model

Ultrasound imaging has become an important method of breast cancer screening due to its non-invasive, low-cost, and ionizing radiation-free characteristics. However, the complexity and uncertainty of ultrasound images (such as speckle noise, morphological diversity of lesion areas, and inter-class similarity) pose challenges to traditional computer-aided diagnosis systems. In response to these issues, this paper proposes a Dynamic Fusion Multi-Scale Feature Interaction Model (DFMFI), specifically designed for the task of benign and malignant breast cancer detection in ultrasound imaging. DFMFI enhances the model's ability to model complex lesion features by combining dynamic feature fusion, multi-scale feature aggregation, and nonlinear dynamic interaction mechanisms. The model includes three core modules: the dynamic feature mixer uses overlapped spatial reduction attention and dynamic depth convolution to efficiently integrate global and local information; the efficient multi-scale feature aggregator captures multi-scale lesion features through a multi-branch structure; the dynamic gated feed forward network enhances the adaptability of feature flow through gating mechanisms and nonlinear reconstruction. Experimental results show that DFMFI significantly outperforms existing methods in terms of classification accuracy, robustness, and computational efficiency, providing an efficient and robust solution for the early screening and diagnosis of breast cancer.