Breast cancer detection using cartesian genetic programming evolved artificial neural networks
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Ahmad:2012:GECCO,
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author = "Arbab Masood Ahmad and Gul Muhammad Khan and
Sahibzada Ali Mahmud and Julian Francis Miller",
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title = "Breast cancer detection using cartesian genetic
programming evolved artificial neural networks",
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booktitle = "GECCO '12: Proceedings of the fourteenth international
conference on Genetic and evolutionary computation
conference",
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year = "2012",
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editor = "Terry Soule and Anne Auger and Jason Moore and
David Pelta and Christine Solnon and Mike Preuss and
Alan Dorin and Yew-Soon Ong and Christian Blum and
Dario Landa Silva and Frank Neumann and Tina Yu and
Aniko Ekart and Will Browne and Tim Kovacs and
Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and
Giovanni Squillero and Nicolas Bredeche and
Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and
Martin Pelikan and Silja Meyer-Nienberg and
Christian Igel and Greg Hornby and Rene Doursat and
Steve Gustafson and Gustavo Olague and Shin Yoo and
John Clark and Gabriela Ochoa and Gisele Pappa and
Fernando Lobo and Daniel Tauritz and Jurgen Branke and
Kalyanmoy Deb",
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isbn13 = "978-1-4503-1177-9",
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pages = "1031--1038",
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keywords = "genetic algorithms, genetic programming, Cartesian
Genetic Programming, real world applications,
Algorithms, Design, Performance, Breast Cancer, Fine
Needle Aspiration, FNA, ANN, Artificial Neural Network,
Neuro-evolution",
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month = "7-11 " # jul,
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organisation = "SIGEVO",
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address = "Philadelphia, Pennsylvania, USA",
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DOI = "doi:10.1145/2330163.2330307",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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size = "8 pages",
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abstract = "A fast learning neuro-evolutionary technique that
evolves Artificial Neural Networks using Cartesian
Genetic Programming (CGPANN) is used to detect the
presence of breast cancer. Features from breast mass
are extracted using fine needle aspiration (FNA) and
are applied to the CGPANN for diagnosis of breast
cancer. FNA data is obtained from the Wisconsin
Diagnostic Breast Cancer website and is used for
training and testing the network. The developed system
produces fast and accurate results when compared to
contemporary work done in the field. The error of the
model comes out to be as low as 1percent for Type-I
(classifying benign sample falsely as malignant) and
0.5percent for Type-II (classifying malignant sample
falsely as benign).",
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notes = "Also known as \cite{2330307} GECCO-2012 A joint
meeting of the twenty first international conference on
genetic algorithms (ICGA-2012) and the seventeenth
annual genetic programming conference (GP-2012)",
- }
Genetic Programming entries for
Arbab Masood Ahmad
Gul Muhammad Khan
Sahibzada Ali Mahmud
Julian F Miller
Citations