Classification of Mammograms Using Cartesian Genetic Programming Evolved Artificial Neural Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Arbab Masood Ahmad and Gul Muhammad Khan and 
                 Sahibzada Ali Mahmud",
  title =        "Classification of Mammograms Using Cartesian Genetic
                 Programming Evolved Artificial Neural Networks",
  booktitle =    "Proceedings 10th IFIP WG 12.5 International Conference
                 Artificial Intelligence Applications and Innovations,
                 AIAI 2014",
  year =         "2014",
  editor =       "Lazaros S. Iliadis and Ilias Maglogiannis and 
                 Harris Papadopoulos",
  volume =       "436",
  series =       "IFIP Advances in Information and Communication
  pages =        "203--213",
  address =      "Rhodes, Greece, September 19-21, 2014",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming, mammogram image classification,
                 GLCM, CGPANN, haralick's parameters",
  isbn13 =       "978-3-662-44654-6",
  DOI =          "doi:10.1007/978-3-662-44654-6_20",
  URL =          "",
  bibdate =      "2014-09-15",
  bibsource =    "DBLP,
  URL =          "",
  abstract =     "We developed a system that classifies masses or
                 microcalcifications observed in a mammogram as either
                 benign or malignant. The system assumes prior manual
                 segmentation of the image. The image segment is then
                 processed for its statistical parameters and applied to
                 a computational intelligence system for classification.
                 We used Cartesian Genetic Programming Evolved
                 Artificial Neural Network (CGPANN) for classification.
                 To train and test our system we selected 2000 mammogram
                 images with equal number of benign and malignant cases
                 from the well-known Digital Database for Screening
                 Mammography (DDSM). To find the input parameters for
                 our network we exploited the overlay files associated
                 with the mammograms. These files mark the boundaries of
                 masses or microcalcifications. A Gray Level
                 Co-occurrence matrix (GLCM) was developed for a
                 rectangular region enclosing each boundary and its
                 statistical parameters computed. Five experiments were
                 conducted in each fold of a 10-fold cross validation
                 strategy. Testing accuracy of 100 percent was achieved
                 in some experiments.",

Genetic Programming entries for Arbab Masood Ahmad Gul Muhammad Khan Sahibzada Ali Mahmud