RESUMO
Objective:To combine automatic image segmentation technology and machine learning methods to accurately classify and recognize mammography images.Methods:Taking mammography images with clustered pleomorphic calcification as the research object, which were in BI-RADS4 class from the Digital Mammogram Database (DDSM). The region of interest (ROI) of the images was automatically segmented. The characteristic features extracted by wavelet transform, Gabor filter and gray level co-occurrence matrix method were fused. The fused feature parameters were screened based on sensitivity analysis. Using ensemble learning method, the polynomial kernel SVM, random forest and logistic regression classifiers were integrated to form a classifier for automatic classification of mammography images. The ensemble learning method was soft voting integration.Results:The proposed ensemble classifier can efficiently recognize and classify mammography images, and its classification sensitivity, specificity and accuracy on the training set were 99.1%, 99.6% and 99.3%, respectively.Conclusions:The proposed mammography image processing, classification and recognition method can provide assistant detection basis for doctors' clinical judgment, and provide a technical basis for subdividing BI-RADS4 class images.