Automatic detection of breast cancer using mathematical morphological operations and machine learning technique

Abstract

A computer-aided diagnosis (CAD) system based on mammograms enables early breast cancer detection, diagnosis, and treatment. However, the accuracy of existing CAD systems remains unsatisfactory. This paper explores a breast CAD method based on feature fusion with Convolutional Neural Network (CNN) deep features. First, we propose a mass detection method based on CNN deep features and Unsupervised Extreme Learning Machine (US-ELM) clustering. Second, we build a feature set fusing deep features, morphological features, texture features, and density features. Third, an ELM classifier is developed using the fused feature set to classify benign and malignant breast masses. Extensive experiments demonstrate the accuracy and efficiency of our proposed mass detection and breast cancer classification method.