Breast cancer detection in digital mammography using phase features and machine learning approach

Abstract

For several years, Computer-Aided Detection (CAD) systems have been used by radiologists as a second interpreter for breast cancer detection in digital mammography. However, for every true-positive cancer detected by a CAD system, more false predictions must be revised by the expert to avoid an unnecessary biopsy. On the other hand, the research community has been exploring different approaches for the detection and classification of breast abnormalities. Machine learning, and particularly deep learning approaches, are being used to analyze digital mammography images. Nevertheless, most of the models proposed so far are trained on a single database and do not have high reliability. In this work, several deep learning models were compared for benign-malign mammography classification. A pre-processing stage is designed to remove noise and extract features using local image phase information. Then, a machine learning approach is utilized for digital mammography classification. Experimental results are presented using various digital mammography datasets and evaluated under different performance metrics.