Application of Support Vector Machines for Breast Calcification Cluster Detection and Mass Classification

Abstract

Breast cancer has become "the most common cancer worldwide", and it is an urgent problem in the medical field to improve the detection rate of breast cancer through early diagnosis and treatment. Based on the current research on medical image processing, our work selects breast images from the MIAS database for pre-processing, segmentation, texture feature extraction and classification, and conducts an in-depth investigation and experimental validation on the theoretical basis of support vector machine algorithm, kernel function selection, and key parameter optimization. The results show the effectiveness of support vector machines in detecting abnormal areas and classifying masses in breast images and provide research ideas for breast cancer diagnosis.