Evolutionary Algorithms for Classification of Mammographic Densities using Local Binary Patterns and Statistical Features

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@InProceedings{Burling-Claridge:2016:CEC,
  author =       "Francine Burling-Claridge and Muhammad Iqbal and 
                 Mengjie Zhang",
  title =        "Evolutionary Algorithms for Classification of
                 Mammographic Densities using Local Binary Patterns and
                 Statistical Features",
  booktitle =    "Proceedings of 2016 IEEE Congress on Evolutionary
                 Computation (CEC 2016)",
  year =         "2016",
  editor =       "Yew-Soon Ong",
  pages =        "3847--3854",
  address =      "Vancouver",
  month =        "24-29 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-5090-0623-6",
  DOI =          "doi:10.1109/CEC.2016.7744277",
  abstract =     "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 neighbour, 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.",
  notes =        "WCCI2016",
}

Genetic Programming entries for Francine Burling-Claridge Muhammad Iqbal Mengjie Zhang

Citations