The Application and Advancement of Intelligent Medical Image Analysis in Breast Cancer Diagnosis

As one of the most common malignant tumors among women worldwide, breast cancer requires early diagnosis and accurate classification to significantly improve patient survival rates. Conventional imaging techniques like mammography, ultrasound, and magnetic resonance imaging (MRI) play a pivotal role in breast cancer screening. Nonetheless, they are constrained by relatively low specificity and a significant dependence on the expertise of medical professionals. In recent years, machine learning and deep learning techniques have provided new approaches for the intelligent diagnosis of breast cancer by extracting high-dimensional features from medical images. This study delves into the pathological aspects, imaging technologies, and the implementation of machine learning algorithms in the context of breast cancer. It conducts a comprehensive review of the diagnostic criteria for non-invasive, early-stage invasive, and fully invasive breast cancers, while also evaluating the strengths and weaknesses of various imaging modalities, including mammography, ultrasound, MRI, and nuclear medicine imaging. The limitations of conventional imaging methods in subtype differentiation are also discussed. Furthermore, by integrating radiomics and deep learning models such as convolutional neural networks (CNN) and random forests, the study evaluates the performance of intelligent diagnostic systems in breast cancer classification. Clinical cases and publicly available datasets were used as data sources. The results show that combining multimodal imaging features with machine learning algorithms significantly improves diagnostic accuracy, achieving an area under the curve (AUC) of 0.922. This research provides theoretical support and technical references for the precise diagnosis and treatment of breast cancer. Future work should focus on enhancing model generalizability and conducting multi-center clinical validation.