Optimized Fuzzy Min-Max Artificial Neural Network Got Cervical Cancer Application

(*) Corresponding author

Authors' affiliations

DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)


In this paper the application of a Fuzzy Min-Max Neural (FMM) network optimized by Genetic Algorithm (GA) for cervical cancer cells is proposed. The proposed system classifies cervical cells as normal, low-grade squamous intra-epithelial lesion (LSIL) and high-grade squamous intra-epithelial lesion (HSIL). The system consists of three stages. In the first stage, cervical cells are segmented using the Adaptive Fuzzy Moving K-means (AFMKM) clustering algorithm. In the second stage, feature extraction is performed where a total of 18 feature where extracted.  Finally in the third stage the extracted features are fed to a FMM with GA Neural Network for classification. The obtained results show that the proposed system can enhance cancer cell classification. To further assess the obtained results the bootstrap hypothesis statistical technique is used to clarify the results.
Copyright © 2013 Praise Worthy Prize - All rights reserved.


Fuzzy Min-Max Neural Network; Genetic Algorithm; Adaptive Fuzzy Moving K-means; Cervical Cancer

Full Text:



L. Farah, N. Farah, and D. Messadeg, "Arabic Literal Amount Recognition by Genetic Fuzzy K Nearest Neighbor Classifier," International Review on Computers and Software vol. 3, pp. 375 - 380, 2008.

Abdelhadi, A., Mouss, L.H., An overview of artificial immune system algorithms for industrial monitoring, (2011) International Review on Computers and Software (IRECOS), 6 (2), pp. 269-274.

P. Lisboa, "Industrial use of safety-related artificial neural networks. ," presented at the Health and Safety Executive Contact Research Report 327, 2001.

E. E. Mangina, S. D. J. McArthur, J. R. McDonald, and A. Moyes, "A multi agent system for monitoring industrial gas turbine start-up sequences," IEEE Transactions on Power Systems, vol. 16, pp. 396 - 401 2001.

T. Munakata, "Commercial and industrial AI," Communications of the ACM, vol. 37, pp. 23-26, 1994.

C. P. Lim, R. F. Harrison, and R. L. Kennedy, "Application of autonomous neural network systems to medical pattern classification tasks," Artificial Intelligence in Medicine, vol. 11, pp. 215-239, 1997.

G. P. K. Economou, C. Spiropoulos, N. M. Economopoulos, N. Charokopos, D. Lymberopoulos, M. Spiliopoulou, et al., "Medical diagnosis and artificial neural networks: a medical expert system applied to pulmonary diseases," in Proceedings of the 1994 IEEE Workshop Neural Networks for Signal Processing 1994, pp. 482-489.

D. West and V. West, "Model Selection for a Medical Diagnosis Decision Support System: A Breast Cancer Detection Case," Artificial Intelligence in Medicine, vol. 20, pp. 183-204, 2000.

T. Kıyan and T. Yıldırım, "Breast Cancer Diagnosis Using Statistical Neural Networks," in International XII. Turkish Symposium on Artificial Intelligence and Neural Networks, 2003, pp. 754-760.

C. S. Pattichis, C. N. Schizas, and L. T. Middleton, "Neural network models in EMG diagnosis," IEEE Transactions on Biomedical Engineering, vol. 42, pp. 486-496, 1995.

N. Mat-Isa, S. Salamah, and U. Ngah, "Adaptive fuzzy moving K-means clustering algorithm for image segmentation," IEEE Trans Consum. Electron. , vol. 55, pp. 2145-2153, 2009.

A. Quteishat, C. P. Lim, and K. S. Tan, "A Modified Fuzzy Min-Max Neural Network with a Genetic-Algorithm-based Rule Extractor for Pattern Classification," IEEE Transactions on systems, Man and Cybernetics Part: A, 2008.

P. K. Simpson, "Fuzzy Min-Max Neural Networks-Part 1: Classification," IEEE Transactions on Neural Networks, vol. 3, pp. 776-786, 1992.

P. K. Simpson, "Fuzzy min-max neural networks - Part 2: Clustering," IEEE Transactions on Fuzzy Systems, vol. 1, pp. 32-45, 1993.

Thomas, J., Kulanthaivel, G., Preterm birth prediction using cuckoo search-based fuzzy min-max neural network, (2013) International Review on Computers and Software (IRECOS), 8 (8), pp. 1854-1862.

G. Carpenter and A. Tan, "Rule Extraction: From Neural Architecture to Symbolic Representation," Connection Science, vol. 7, pp. 3-27, 1995.

H. Ishibuchi, K. Nozaki, and N. Yamamoto, "Selecting fuzzy if-then rules for classification problems using genetic algorithms," IEEE Transactions on Fuzzy Systems, vol. 3, pp. 260-270, 1995.

H. Ishibuchi, T. Murata, and I. B. Turksen, "Single-Objective and Two-Objective Genetic Algorithms for Selecting Linguistic Rules for Pattern Classification Problems," Fuzzy Sets and Systems, vol. 89, pp. 135-150, 1997.

Gan G, Ma C, and W. J, Data clustering: theory, algorithms, and applications: SIAM, 2007.

S. Hojjatoleslami and J. Kittler, " Region growing: A new approach," IEEE Trans. Image Process, vol. 7, pp. 1079-1084, 1998.

Y. Chang and X. Li, "Adaptive image region-growing," IEEE Trans.Image Process., vol. 3, pp. 868-872, 1994.

R. Adams and L. Bischof, "Seeded region growing," IEEE Trans. Patt. Anal. Mach. Intell, pp. 641-647, 1994.

N. Mat-Isa, "Automated Edge Detection Technique for Pap Smear Images Using Moving K Means Clustering and Modified Seed Based Region Growing Algorithm," Int. J. Comput. The Internet Manage, vol. 13, pp. 45-59, 2005.

W. Zhi and H. Sai-Xian, "An adaptive edge-detection method based on Canny algorithm " Image Graphics, p. 8, 2004.

Z. Yuqian, G. Weihua, and C. Zhencheng, "Edge Detection Based on Multi-Structure Elements Morphology," in The Sixth World Congress on Intelligent Control and Automation, 2006, pp. 9795-9798.

T. Kanungo, D. Mount, N. Netanyahu, C. Piatko, R. Silverman, and A. Wu, "An efficient k-means clustering algorithm: Analysis and implementation," IEEE Trans. Patt. Anal. Mach. Intell., vol. 24, pp. 881-892, 2002.

S. Revathy and B. Parvathavarthini, "Rough Fuzzy Clustering Algorithm Using Fuzzy Rough Correlation Factor," International Review on Computers and Software, vol. 8, pp. 2303-2308, 2013.

M. Mashor, " Hybrid training algorithm for RBF network," International Journal of The Computer, The Internet and Manage., vol. 8, pp. 50-65, 2000.

N. Mustafa, N. Mat-Isa, M. Mashor, and N. Othman, "Capability Of New Features Of Cervical Cells For Cervical Cancer Diagnostic System Using Hierarchical Neural Network," International Journal of Simulation, Systems, Science and Technology, vol. 9, pp. 56-64, 2008.

A. Weeks, Fundamentals of ElectronicsImage Processing. Washington: Spie OpticalEngineering Press, 1996.

C. Zhang and P. Wang, "A New Method ofColor Image Segmentation Based on Intensity andHue Clustering," presented at the Proc. of 15th Int. Conf. on PatternRecognition, 2000.

J. Carpenter and J. Bithell, "Bootstrap Confidence Intervals: When, Which, What? A Practical Guide for Medical Statisticians," Statistics in Medicine, vol. 19, pp. 1141-1164, 2000.

B. Efron, "Bootstrap methods: Another look at the jackknife," Annals of Statistics, vol. 7, pp. 1-26, 1979.


  • There are currently no refbacks.

Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize