Evolutionary algorithms for classification of mammographie densities using local binary patterns and statistical features
Francine Burling-Claridge, Muhammad Iqbal, Mengjie Zhang · 2016
Millions of women are suffering from breast cancer, which can be treated effectively if it is detected early. Breast density is one of the many factors that lead to an increased risk of breast cancer for women. However, it is difficult for radiologists to provide both accurate and uniform evaluations of different density levels in a large number of mammographic images generated in the screening process. Various computer aided diagnosis systems for digital mammograms have been reported in literature, but very few of them thoroughly investigate mammographic densities. This study presents a thorough analysis of classifying mammographic densities using different local binary patterns and statistical features of digital mammograms in two evolutionary algorithms, i.e., genetic programming and learning classifier systems; and four conventional classification methods, i.e., naive Bayes, decision trees, K-nearest neighbor, and support vector machines. The obtained results show that evolutionary algorithms have potential to solve these challenging real-world tasks. It is found that statistical features produced better results than local binary patterns for the experiments conducted in this study. Further, in genetic programming, the reuse of extracted knowledge from one feature set to another shows statistically significant improvement over the standard approach.