Evolutionary Algorithms for Classification of Mammographic Densities using Local Binary Patterns and Statistical Features
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Burling-Claridge:2016:CEC,
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author = "Francine Burling-Claridge and Muhammad Iqbal and
Mengjie Zhang",
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title = "Evolutionary Algorithms for Classification of
Mammographic Densities using Local Binary Patterns and
Statistical Features",
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booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary
Computation (CEC 2016)",
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year = "2016",
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editor = "Yew-Soon Ong",
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pages = "3847--3854",
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address = "Vancouver",
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month = "24-29 " # jul,
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-5090-0623-6",
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DOI = "doi:10.1109/CEC.2016.7744277",
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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.",
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notes = "WCCI2016",
- }
Genetic Programming entries for
Francine Burling-Claridge
Muhammad Iqbal
Mengjie Zhang
Citations