Abstract
Cartesian Genetic Programming (CGP) is now attracting considerable recognition as an evolutionary algorithm that not only delivers high performance, but one that has a representation that is flexible and easy to adapt to a range of applications. Problems based in medicine stand to benefit greatly due their diverse and highly non-linear nature, which can exploit this flexibility. This chapter aims to give an overview of the types of medical problems that may be addressed and illustrates this by considering in detail, a number of published case examples. Finally, for EC practitioners, some advice on the common pit falls, benefits and rewards of medical applications and, specifically, obtaining patient data is offered at the end of the chapter.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Cancer Research UK. http://info.cancerresearchuk.org/cancerstats/types/breast/incidence/, 27-1-2011 ed.
V. Andolina, S. Lillé, and K. M. Willison, Mammographic Imaging: A Practical Guide. Lippincott Williams and Wilkins, 2001.
H. D. Cheng, X. Cai, X. Chen, L. Hu, and X. Lou, “Computer-aided detection and classification of microcalcifications in mammograms: a survey,” Pattern Recognition, vol. 36, pp. 2967–2991, 2003.
D. B. Kopans, Breast Imaging. Lippincott Williams and Wilkins, 2006.
American College of Radiology, Breast I Reporting and Data System Atlas (BI-RADS): Mammography, 4 ed., 2003.
R. Gonzalez and R. Woods, Digital Image Processing. Prentice Hall, 2002.
J. C. Fu, S. K. Lee, S. T. C. Wong, J. Y. Yeh, A. H. Wang, and H. K. Wu, “Image segmentation feature selection and pattern classification for mammographic microcalcifications,” Computerized Medical Imaging and Graphics, vol. 29, pp. 419–429, 2005.
M. Gavrielides, J. Lo, R. Vargas-Voracek, and C. Floyd, “Segmentation of suspicious clustered microcalcifications in mammograms,” Medical Physics, vol. 27, pp. 13–22, 2000.
J. Kim and H. Park, “Statistical Textural Features for Detection of Microcalcifications in Digitized Mammograms,” IEEE Transactions Medical Imaging, vol. 18, no. 3, pp. 231–238, 1999.
D. Hope, S. L. Smith, and E. Munday, “Evolutionary Algorithms in the Classification of Mammograms,” in IEEE Symposium on Computational Intelligence in Image and Signal Processing, pp. 258–265, 2007.
K. Völk, J. F. Miller, and S. L. Smith, “Multiple Network CGP for the Classification of Mammograms,” in Applications of Evolutionary Computing, vol. 5484 of LNCS, pp. 405–413, 2009.
J. A. Walker, K. Völk, S. L. Smith, and J. F. Miller, “Parallel evolution using multi-chromosome Cartesian genetic programming,” Genetic Programming and Evolvable Machines, vol. 10, no. 4, pp. 417–445, 2009.
J. F. Miller and S. L. Smith, “Redundancy and Computational Efficiency in Cartesian Genetic Programming,” IEEE Transactions on Evolutionary Computation, vol. 10, no. 2, pp. 167–174, 2006.
Parkinson’s UK. http://www.parkinsons.org.uk/about_parkinsons/what_is_parkinsons.aspx, 28-1-11 ed.
Parkinson’s Disease Foundation. http://www.pdf.org/en/parkinson_statistics, 28-1-11 ed.
C. Levine, K. R. Fahrbach, A. D. Siderowf, R. P. Estok, V. M. Ludensky, and S. D. Ross, Diagnosis and Treatment of Parkinson’s Disease: A Systematic Review of the literature. No. 57, Agency for Healthcare Research and Quality, 2003.
S. L. Smith, P. Gaughan, D. M. Halliday, Q. Ju, N. M. Aly, and J. R. Playfer, “Diagnosis of Parkinson’s Disease using Evolutionary Algorithms,” Genetic Programming and Evolvable Machines, vol. 8, pp. 433–447, 2007.
The main symptoms of Parkinson’s. http://www.parkinsons.org.uk/about_parkinsons/signs_and_symptoms/the_main_symptoms.aspx, 23-2-11 ed.
A. Berardelli, J. C. Rothwell, P. D. Thompson, and M. Hallett, “Pathophysiology of bradykinesia in Parkinson’s disease,” Brain, vol. 124, no. 11, pp. 2131–2146, 2001.
M. A. Lones, Enzyme Genetic Programming. PhD thesis, University of York, 2003.
M. A. Lones and A. M. Tyrrell, “Biomimetic representation with enzyme genetic programming,” Genetic Programming and Evolvable Machines, vol. 3, no. 2, pp. 193–217, 2002.
M. A. Lones and A. M. Tyrrell, “Modelling biological evolvability: Implicit context and variation filtering in enzyme genetic programming,” BioSystems, vol. 76, no. 2, pp. 229–238, 2004.
S. L. Smith, S. Leggett, and A. M. Tyrrell, “An implicit context representation for evolving image processing filters,” in Proc. Workshop on Evolutionary Computation in Image Analysis and Signal Processing, vol. 3449 of LNCS, pp. 407–416, 2005.
M. A. Lones and A. M. Tyrrell, “Enzyme genetic programming,” in Proc. of the Congress on Evolutionary Computation (J.-H. Kim, B.-T. Zhang, G. Fogel, and I. Kuscu, eds.), vol. 2, pp. 1183–1190, IEEE Press, 2001.
A. Hazell and S. L. Smith, “Towards an Objective Assessment of Alzheimer’s Disease: The Application of a Novel Evolutionary Algorithm in the Analysis of Figure Copying Tasks,” in Proc. GECCO Workshop on Medical Applications of Genetic and Evolutionary Computation, 2008.
M. Knapp and M. Prince, Dementia UK: A report to the Alzheimer’s Society on the prevalence and economic cost of dementia in the UK. Alzheimer’s Society, 2007.
C. Ferri, M. Prince, C. Brayne, H. Brodaty, L. Fratiglioni, M. Ganguli, K. Hall, K. Hasegawa, H. Hendrie, Y. Huang, A. Jorm, C. Mathers, P. R. Menezes, E. Rimmer, and M. Scazufca, “Global prevalence of dementia: A Delphi consensus study,” The Lancet, vol. 266, no. 9503, pp. 2112–2117, 2006.
K. Blennow, M. Leon, and H. Zetterberg, “Alzheimer’s disease,” The Lancet, vol. 268, pp. 387–403, 2006.
W. Wolfson, “Unraveling the Tangled Brain of Alzheimer’s,” Chemistry and Biology, vol. 15, no. 2, pp. 89–90, 2008.
G. McKhann, D. Drachman, M. Folstein, R. Katzman, D. Price, and E. M. Stadlan, “Clinical diagnosis of Alzheimer’s disease: Report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease,” Neurology, vol. 34, pp. 939–944, 1984.
Y. Shimada, K. Meguro, M. Kasai, M. Shimada, S. Yamaguchi, and A. Yamadori, “Necker cube copying ability in normal elderly and Alzheimer’s disease. A community-based study: The Tajiri project,” Psychogeriatrics, vol. 6, pp. 4–9, 2006.
J. Bremner, R. Morse, S. Hughes, and G. Andreasen, “Relations between drawing cubes and copying line diagrams of cubes in 7-to 10-year old children,” Child Development, vol. 71, no. 3, pp. 621–634, 2000.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Smith, S.L., Walker, J.A., Miller, J.F. (2011). Medical Applications of Cartesian Genetic Programming. In: Miller, J. (eds) Cartesian Genetic Programming. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17310-3_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-17310-3_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17309-7
Online ISBN: 978-3-642-17310-3
eBook Packages: Computer ScienceComputer Science (R0)