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An Organized Committee of Artificial Neural Networks in the Classification of Human Chromosomes

by Sadina Gagula-palalic, Mehmet Can
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 80 - Number 8
Year of Publication: 2013
Authors: Sadina Gagula-palalic, Mehmet Can
10.5120/13884-1789

Sadina Gagula-palalic, Mehmet Can . An Organized Committee of Artificial Neural Networks in the Classification of Human Chromosomes. International Journal of Computer Applications. 80, 8 ( October 2013), 38-41. DOI=10.5120/13884-1789

@article{ 10.5120/13884-1789,
author = { Sadina Gagula-palalic, Mehmet Can },
title = { An Organized Committee of Artificial Neural Networks in the Classification of Human Chromosomes },
journal = { International Journal of Computer Applications },
issue_date = { October 2013 },
volume = { 80 },
number = { 8 },
month = { October },
year = { 2013 },
issn = { 0975-8887 },
pages = { 38-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume80/number8/13884-1789/ },
doi = { 10.5120/13884-1789 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:54:02.985637+05:30
%A Sadina Gagula-palalic
%A Mehmet Can
%T An Organized Committee of Artificial Neural Networks in the Classification of Human Chromosomes
%J International Journal of Computer Applications
%@ 0975-8887
%V 80
%N 8
%P 38-41
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Neural networks are organized in committees to improve the correctness of the decisions created by artificial neural networks (ANN's). In the classification of human chromosomes, it is accustomed to use multilayer perceptrons with multiple (22-24) outputs. Because of the huge number of synaptic weights to be tuned, these classifiers cannot go beyond a level of 92% overall correctness. In this study we represent a special organized committee of 462 simple perceptrons to improve the rate of correct classification of 22 types of human chromosomes. Each of these simple perceptrons is trained to distinguish between two types of chromosomes. When a new data is entered, the votes of these 462 simple perceptrons and additional 22 dummy perceptrons create a decision matrix of the size 22×22. By a special assembling of these votes we get a higher rate of correct classification of 22 types of human chromosomes.

References
  1. Mitchell T. M. , "Machine Learning", McGraw-Hill, pp. 81-116, 1997
  2. Haykin S. , "Neural Networks: A Comprehensive Foundation", Second Edition, Prentice-Hall, Inc. , Simon & Schuster, 1999
  3. Lu, Y. and Y. Ya, "An expert system for banded chromosomes recognition", Proceedings of the Annual International Conference of the IEEE Engineering in Engineering Medicine and Biology Society (IEMBS), Seattle, WA, pp. 1789-1790, 9-12 November, 1989
  4. Wu, Q. , P. Suetens and A. Oosterlinck, "Chromosome classification using a multi-layer perception neural network", Proceedings of the 12th Annual International Conference of IEEE Engineering in Medicine and Biology Society, pp. 1453-1454, Vol. 12, No. 3, 1990
  5. Errington P. A. , Graham J. , "Classification of Chromosomes using a Combination of Neural Networks", IEEE International Conference on Neural Networks (ICNN), San Francisco, CA, pp. 1236-1241, Vol. 3, 28th March – 1st April, 1993
  6. Delshadpour S. , "Reduced size multilayer perceptron neural network for human chromosome classification", Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEMBS), pp. 2249-2252, Vol3, 17-21 September, 2003
  7. El Emary I. M. M. , "On the Application of Artificial Neural Networks in Analyzing and Classifying the Human Chromosomes", Journal of Computer Science 2 (1): 72-75, 2006
  8. Wang X. , Zheng B. , Li S. , Mulvihill J. J. , Wood M. C. , and Liu H. , "Automated Classification of Metaphase Chromosomes: Optimization of an Adaptive Computerized Scheme", Journal of Biomedical Informatics, 42 (1): 22–31, 2009
  9. Can, M. and Gagula-Palalic S. , "Application of Ensemble Machines of Neural Networks to Chromosome Classification", South East Journal of Soft Computing (SEJSC), Vol. 1, No. 2, pp. 31-35, 2012
  10. Ruan X. , "A classifier with the fuzzy Hopfield network for human chromosomes", Proceedings of the 3rd World Congress on Intelligent Control and Automation, Hefei, Vol. 2, pp. 1159-1164, 28 Jun – 2 Jul, 2000
  11. Ruspini E. , "A Fast Method for Probabilistic and Fuzzy Cluster Analysis using Association Measures", Proceedings of the 6th International Conference on System Sciences, Hawaii, pp. 56–58, 1973
  12. Ramstein G. , Bernadet M. , Kangoud A. and Barba D. , "A rule-based image analysis system for chromosome classification", Proceedings of the 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEMBS), Paris, Vol. 3, pp. 926-927, 29 Oct – 1 Nov, 1992
  13. Keller J. M. , Gader P. , Sjahputera O. and Caldwell C. W. , "A fuzzy logic rule based system for chromosome recognition", Proceedings of the 8th IEEE Symposium on Computer-Based Medical Systems (CMBS), Lubbock, TX, pp. 125-132, 9 – 10 Jun, 1995
  14. Sjahputera O. and Keller J. M. , "Evolution of a fuzzy rule-based system for automatic chromosome recognition", IEEE International Fuzzy Systems Conference Proceedings, Seoul, South Korea, pp. 129-134,22 – 25 Aug, 1999
  15. Sarosa M. , Ahmad A. S. , Riyanto B. and Noer A. S. , "Optimization of Neuro-Fuzzy System Using Genetic Algorithm for Chromosome Classification", ITB Journal of Information and Communication Technology, Vol. 1, No. 1, pp. 56-69, 2007
  16. Lerner B. , Guterman H. , Aladjem M. and Dinstein I. , "A comparative study of neural network based feature extraction paradigms", Pattern Regognition Letters 20, pp. 7-14, 1999
  17. Kyan M. J. , Guan L. , Amison M. R. and Cogswell C. J. , "Feature extraction of chromosomes from 3D confocal microscope images", IEEE Transactions on Biomedical Engineering, Vol. 48, No. 11, November, 2001
  18. Mousavi P. , Fels S. S. , Ward R. K. and Lansdorp P. M. , "Classification of homologous human chromosomes using mutual information maximization", International Conference on Image Processing, Thessaloniki, pp. 845-848, Vol. 2, 7 – 10 Oct, 2001
  19. Lundsteen, C. , Phillip J. and Granum E. , "Quantitative analysis of 6985 digitized trypsin G-banded human metaphase chromosomes", Clinical Genetics, 18, pp. 355-370, 1980
  20. Cho J. , "Chromosome classification using back propagation neural networks", IEEE Engineering in Medicine and Biology, Vol. 19, pp. 28-33, 2000
  21. Granum E. and Thomason M. G. , "Automatically Inferred Markov Network Models for Classification of Chromosomal Band Pattern Structures", Cytometry 11, pp. 26-39, 1990
  22. Sweeney W. P. and M. T. Musavi, "Application of neural networks for chromosome classification", Proceedings of the 15th Annual International Conference of IEEE Engineering in Medicine and Biology Society, pp: 239-240, 1993
  23. Conroy J. M. , Tamara G. K. , O'Leary D. P. and O'Leary, T. J. , "Chromosome Identification Using Hidden Markov Models: Comparison with Neural Networks, Singular Value Decomposition, Principal Components Analysis, and Fisher Discriminant Analysis", Center for Computing Sciences, Institute for Defense Analyses, Bowie, Maryland, USA, 2000
  24. Schwartzkopf, W. C. , "Maximum Likelihood Techniques for Joint Segmentation-Classification of Multi-spectral Chromosome Images", Doctoral Dissertation, The Faculty of the Graduate School of The University of Texas at Austin, 2002
Index Terms

Computer Science
Information Sciences

Keywords

Classification of human chromosomes perceptrons committee machines image profile metaphase

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