Evolving Digital Circuits for Early Stage Breast Cancer Detection Using Cartesian Genetic Programming
Cartesian Genetic Programming (CGP) is explored to
design an optimal circuit capable of early stage breast cancer
detection. CGP is used to evolve simple multiplexer circuits for
detection of malignancy in the Fine Needle Aspiration (FNA) samples
of breast. The data set used is extracted from Wisconsins Breast
Cancer Database (WBCD). A range of experiments were performed,
each with different set of network parameters. The best evolved
network detected malignancy with an accuracy of 99.14%, which is
higher than that produced with most of the contemporary non-linear
techniques that are computational expensive than the proposed
system. The evolved network comprises of simple multiplexers
and can be implemented easily in hardware without any further
complications or inaccuracy, being the digital circuit.
[1] Breast Cancer Wisconsin (Diagnostic) Data Set.
http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic).
[2] http://mlr.cs.umass.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29.
[3] Hong Guo and Asoke K. Nandi. Breast cancer diagnosis using genetic
programming generated feature. Pattern Recognition, 39(5):980-987,
2006.
[4] J. Han and N. Cercone. ule Viz: a model for visualizing knowledge
discovery process. In KDD, pages 244-253, 2000.
[5] Seral Sahan, Kamal polat, Halife Kodaz and Salih Gunes. Hybrid method
based on fuzz-immune system and k-nn algorithm for Breast Cancer
Diagnosis, 37(2007) 415-423.
[6] J. R. Quinlan, Improved use of continuous attributes in C4.5, J. Artif.
Intell. Res 4(1996) 77-90.
[7] H. J. Hamilton, N. Shan, N.Cerone. RIAC: a rule induction algorithm
based on approximate classification, Technical Report CS 96-06,
University of regina, 1996.
[8] B. Ster, A. Dobinka, Neural networks in medical diagnosis: comparison
with other methods, in: Proceedings of International Conference on
Engineering Applications of Neural Networks (EANN 96), 1996, pp.
427-430.
[9] K. P. Bennet, J. A. Blue, A support vector machine approach to decision
trees, Math Report, vols. 97-100, Rensselaer Polytechnic Institute.
[10] D. Nauck, R. Kruse, obtaining interpretable fuzzy classification rules
from medical data Artif. Intell-Med. 16(1999) 149-169.
[11] C. A. Pena-Reyes, M.Sipper, A fuzzy-genetic approach to breast Cancer
diagnosis, Artif. Intell. Med. 17(1999). 131-155.
[12] R. Setiono, Generating concise and accurate classification rules for
Breast Cancer Diagnosis, Artif. Intell. Med.18(2000) 205-219.
[13] D. E. Goodman, L.Boggess, A. Watkins, Artificial immune System
Classification of multipleclass problems, in: Proceedings of artificial
Neural Networks in Engineering ANNIE (2002), 2002, pp.179-183
[14] J. Abonyi, F. Szeifert, Supervised Fuzzy clustering for the identification
of fuzzy classifiers, Pattern Recognition Lett. 24(2003) 2195-2207.
[15] W. H. Land Jr, L. Albertelli, Y. Titkov, P. Kaltsatis, and G. Seburyano.
Evolution of neural networks for the detection of breast cancer. In Proc.
IEEE. Int. Joint Symposia on Intelligence and Systems, INTSYS ’98,
pages 34-, 1998.
[16] Hussein A. Abbass, An evolutionary artificial neural networks
approach for breast cancer diagnosis. Artificial Intelligence in Medicine,
25:265-281, 2002.
[17] R. Janghel, Anupam Shukla, Ritu Tiwari, and Rahul Kala. Intelligent
Decision Support System for Breast Cancer. In Advances in Swarm
Intelligence, volume 6146, chapter 46, pages 51-358. Springer Berlin
Heidelberg, Berlin, Heidelberg, 2010.
[18] Hai H. Dam, Hussein Abbass, Chris Lokan, Xin Yao, et al. Neural-based
learning classifier Systems. Knowledge and Data Engineering, IEEE
Transactions on, 20 (1):26-39, 2008.
[19] Md Monirul Islam, Xin Yao, S. M. Shahriar Nirjon, Muhammad Asiful
Islam, andKazuyuki Murase. Bagging and boosting negatively correlated
neural networks.Systems, Man, and Cybernetics, Part B: Cybernetics,
IEEE Transactions on, 38 (3):771-784, 2008
[20] Arbab Masood Ahmad, Gul Muhammad Khan, and Sahibzada
Ali Mahmud. Classification of mammograms using cartesian genetic
programming evolved artificial neural networks. In Artificial Intelligence
Applications and Innovations, pages 203-213. Springer, 2014 [21] Khan, Maryam Mashal and Ahmed, Arbab Masood and Khan, Gul
Muhammad and Miller, Julian. Fast learning Neural Networks using
Cartesian Genetic Programming.Neurocomputing, Volume 121, pages
274-289,Elsevier,2013.
[22] Chen, X., and Hurst, S. (1982). A comparison of universal-logic-module
realizations and their application in the synthesis of combinatorial and
sequential logic networks. IEEE Transactions on Computers, 31, 140-147.
[23] T. Higuchi et al., Real-World Applications of Analog and Digital
Evolvable Hardware, IEEE Transactions on Evolutionary Computation,
vol 3, no 3, pp 220-235, Sept 99.
[24] A. Thompson, On the Automatic Design of Robust Electronics Through
Artificial Evolution in Proceedings of the International Conference on
Evolvable Systems: from Biology to Hardware, pp13-24, 1998.
[25] Lichman, M. (2013). UCI Machine Learning Repository
[http://archive.ics.uci.edu/ml]. Irvine, CA: University of California,
School of Information and Computer Science.
[1] Breast Cancer Wisconsin (Diagnostic) Data Set.
http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic).
[2] http://mlr.cs.umass.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29.
[3] Hong Guo and Asoke K. Nandi. Breast cancer diagnosis using genetic
programming generated feature. Pattern Recognition, 39(5):980-987,
2006.
[4] J. Han and N. Cercone. ule Viz: a model for visualizing knowledge
discovery process. In KDD, pages 244-253, 2000.
[5] Seral Sahan, Kamal polat, Halife Kodaz and Salih Gunes. Hybrid method
based on fuzz-immune system and k-nn algorithm for Breast Cancer
Diagnosis, 37(2007) 415-423.
[6] J. R. Quinlan, Improved use of continuous attributes in C4.5, J. Artif.
Intell. Res 4(1996) 77-90.
[7] H. J. Hamilton, N. Shan, N.Cerone. RIAC: a rule induction algorithm
based on approximate classification, Technical Report CS 96-06,
University of regina, 1996.
[8] B. Ster, A. Dobinka, Neural networks in medical diagnosis: comparison
with other methods, in: Proceedings of International Conference on
Engineering Applications of Neural Networks (EANN 96), 1996, pp.
427-430.
[9] K. P. Bennet, J. A. Blue, A support vector machine approach to decision
trees, Math Report, vols. 97-100, Rensselaer Polytechnic Institute.
[10] D. Nauck, R. Kruse, obtaining interpretable fuzzy classification rules
from medical data Artif. Intell-Med. 16(1999) 149-169.
[11] C. A. Pena-Reyes, M.Sipper, A fuzzy-genetic approach to breast Cancer
diagnosis, Artif. Intell. Med. 17(1999). 131-155.
[12] R. Setiono, Generating concise and accurate classification rules for
Breast Cancer Diagnosis, Artif. Intell. Med.18(2000) 205-219.
[13] D. E. Goodman, L.Boggess, A. Watkins, Artificial immune System
Classification of multipleclass problems, in: Proceedings of artificial
Neural Networks in Engineering ANNIE (2002), 2002, pp.179-183
[14] J. Abonyi, F. Szeifert, Supervised Fuzzy clustering for the identification
of fuzzy classifiers, Pattern Recognition Lett. 24(2003) 2195-2207.
[15] W. H. Land Jr, L. Albertelli, Y. Titkov, P. Kaltsatis, and G. Seburyano.
Evolution of neural networks for the detection of breast cancer. In Proc.
IEEE. Int. Joint Symposia on Intelligence and Systems, INTSYS ’98,
pages 34-, 1998.
[16] Hussein A. Abbass, An evolutionary artificial neural networks
approach for breast cancer diagnosis. Artificial Intelligence in Medicine,
25:265-281, 2002.
[17] R. Janghel, Anupam Shukla, Ritu Tiwari, and Rahul Kala. Intelligent
Decision Support System for Breast Cancer. In Advances in Swarm
Intelligence, volume 6146, chapter 46, pages 51-358. Springer Berlin
Heidelberg, Berlin, Heidelberg, 2010.
[18] Hai H. Dam, Hussein Abbass, Chris Lokan, Xin Yao, et al. Neural-based
learning classifier Systems. Knowledge and Data Engineering, IEEE
Transactions on, 20 (1):26-39, 2008.
[19] Md Monirul Islam, Xin Yao, S. M. Shahriar Nirjon, Muhammad Asiful
Islam, andKazuyuki Murase. Bagging and boosting negatively correlated
neural networks.Systems, Man, and Cybernetics, Part B: Cybernetics,
IEEE Transactions on, 38 (3):771-784, 2008
[20] Arbab Masood Ahmad, Gul Muhammad Khan, and Sahibzada
Ali Mahmud. Classification of mammograms using cartesian genetic
programming evolved artificial neural networks. In Artificial Intelligence
Applications and Innovations, pages 203-213. Springer, 2014 [21] Khan, Maryam Mashal and Ahmed, Arbab Masood and Khan, Gul
Muhammad and Miller, Julian. Fast learning Neural Networks using
Cartesian Genetic Programming.Neurocomputing, Volume 121, pages
274-289,Elsevier,2013.
[22] Chen, X., and Hurst, S. (1982). A comparison of universal-logic-module
realizations and their application in the synthesis of combinatorial and
sequential logic networks. IEEE Transactions on Computers, 31, 140-147.
[23] T. Higuchi et al., Real-World Applications of Analog and Digital
Evolvable Hardware, IEEE Transactions on Evolutionary Computation,
vol 3, no 3, pp 220-235, Sept 99.
[24] A. Thompson, On the Automatic Design of Robust Electronics Through
Artificial Evolution in Proceedings of the International Conference on
Evolvable Systems: from Biology to Hardware, pp13-24, 1998.
[25] Lichman, M. (2013). UCI Machine Learning Repository
[http://archive.ics.uci.edu/ml]. Irvine, CA: University of California,
School of Information and Computer Science.
@article{"International Journal of Electrical, Electronic and Communication Sciences:77950", author = "Zahra Khalid and Gul Muhammad Khan and Arbab Masood Ahmad", title = "Evolving Digital Circuits for Early Stage Breast Cancer Detection Using Cartesian Genetic Programming", abstract = "Cartesian Genetic Programming (CGP) is explored to
design an optimal circuit capable of early stage breast cancer
detection. CGP is used to evolve simple multiplexer circuits for
detection of malignancy in the Fine Needle Aspiration (FNA) samples
of breast. The data set used is extracted from Wisconsins Breast
Cancer Database (WBCD). A range of experiments were performed,
each with different set of network parameters. The best evolved
network detected malignancy with an accuracy of 99.14%, which is
higher than that produced with most of the contemporary non-linear
techniques that are computational expensive than the proposed
system. The evolved network comprises of simple multiplexers
and can be implemented easily in hardware without any further
complications or inaccuracy, being the digital circuit.", keywords = "Breast cancer detection, cartesian genetic
programming, evolvable hardware, fine needle aspiration (FNA).", volume = "12", number = "9", pages = "651-7", }
