Abstract

Breast cancer is the most common cause of death in women and the second leading cause of cancer deaths worldwide. Primary prevention in early stages leads to the possibility of taking opportune actions since characteristic footprints of the disease can be found through image processing. In this paper, we propose an approach to computationally perform mammograms images classification based on a combined classifier. The combined classifier, which is formed by Support Vector Machines and Naïve Bayes machine learning, results in reliable image classification thus making it a valuable tool for mammogram diagnostics.

© 2012 Optical Society of America