Classification of Mammograms Using Cartesian Genetic Programming Evolved Artificial Neural Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@InProceedings{conf/ifip12/AhmadKM14,

• 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 Technology",
• 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 = "http://dx.doi.org/10.1007/978-3-662-44654-6_20",
• bibdate = "2014-09-15",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ifip12/aiai2014.html#AhmadKM14",
• URL = "http://dx.doi.org/10.1007/978-3-662-44654-6",
• 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

Citations