Benign and Malignant Breast Mass Detection and Classification in Digital Mammography: The Effect of Subtracting Temporally Consecutive Mammograms

Abstract

Breast cancer remains one of the leading cancers worldwide and is the main cause of death in women with cancer. Effective early-stage diagnosis can reduce the mortality rates of breast cancer. Currently, mammography is the most reliable screening method and has significantly decreased the mortality rates of these malignancies. However, accurate classification of breast abnormalities using mammograms is especially challenging, driving the development of Computer-Aided Diagnosis (CAD) systems. In this work, subtraction of temporally consecutive digital mammograms and machine learning were combined, to develop an algorithm for the automatic detection and classification of benign and malignant breast masses. A private dataset was collected specifically for this study. A total of 196 images were gathered, from 49 patients (two time points and two views of each breast), with precisely annotated mass locations and biopsy confirmed malignant cases. For the classification, ninety-six features were extracted and five feature selection techniques were combined. Ten classifiers were tested, using leave-one-patient-out and 7-fold cross-validation. The classification performance reached 91.7% sensitivity, 89.7% specificity and 90.8% accuracy, using Neural Networks, an improvement, compared to the state-of-the-art algorithms that utilized sequential mammograms for the classification of benign and malignant breast masses. This work demonstrates the effectiveness of combining subtraction of temporally sequential digital mammograms, along with machine learning, for the automatic classification of benign and malignant breast masses.