Frontiers in Signal Processing
An Internal Clustering Validation Based Fitness Approach for Meta-Heuristic Diagnosis of Cervical Cancer
Download PDF (1045.3 KB) PP. 57 - 67 Pub. Date: April 11, 2020
Author(s)
- M. Kerem Un
Faculty of Engineering and Architecture, Department of Biomedical Engineering, Cukurova University, Adana 01330, Balcali, Turkey - Mustafa Guven
Faculty of Engineering and Architecture, Department of Biomedical Engineering, Cukurova University, Adana 01330, Balcali, Turkey - Caglar Cengizler*
Faculty of Engineering and Architecture, Department of Biomedical Engineering, Cukurova University, Adana 01330, Balcali, Turkey - Seyda Erdogan
Faculty of Medicine, Department of Pathology, Cukurova University, Adana 01330, Balcali, Turkey - Aysun Uguz
Faculty of Medicine, Department of Pathology, Cukurova University, Adana 01330, Balcali, Turkey
Abstract
Keywords
References
[1] U. Maulik and S. Bandyopadhyay, “Genetic algorithm-based clustering technique,” Pattern recognition, vol. 33, no. 9, pp. 1455–1465, 2000.
[2] T. Jiang and S. De Ma, “Cluster analysis using genetic algorithms,” in Signal Processing, 1996., 3rd International Conference on, vol. 2. IEEE, 1996, pp. 1277–1279.
[3] C. A. Murthy and N. Chowdhury, “In search of optimal clusters using genetic algorithms,” 1996.
[4] J. H. Holland, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press, 1992.
[5] C. Raposo, C. H. Antunes, and J. P. Barreto, “Automatic clustering using a genetic algorithm with new solution encoding and operators,” in International Conference on Computational Science and Its Applications. Springer, 2014, pp. 92–103.
[6] E. R. Hruschka, R. J. Campello, A. A. Freitas et al., “A survey of evolutionary algorithms for clustering,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 39, no. 2, pp. 133–155, 2009.
[7] P. Scheunders, “A genetic c-means clustering algorithm applied to color image quantization,” Pattern recognition, vol. 30, no. 6, pp. 859–866, 1997.
[8] A. Li, “The operator of genetic algorithms to improve its properties,” Modern Applied Science, vol. 4, no. 3, p. 60, 2010.
[9] S. Bandyopadhyay and U. Maulik, “Genetic clustering for automatic evolution of clusters and application to image classification,” Pattern recognition, vol. 35, no. 6, pp. 1197–1208, 2002.
[10] V. Roth and T. Lange, “Feature selection in clustering problems,” in Advances in neural information processing systems, 2004, pp. 473–480.
[11] M. E. Plissiti, C. Nikou, and A. Charchanti, “Combining shape, texture and intensity features for cell nuclei extraction in pap smear images,” Pattern Recognition Letters, vol. 32, no. 6, pp. 838–853, 2011.
[12] M. Guven and C. Cengizler, “Data cluster analysis-based classification of overlapping nuclei in pap smear samples,” Biomedical engineering online, vol. 13, no. 1, p. 159, 2014.
[13] E. Bengtsson and P. Malm, “Screening for cervical cancer using automated analysis of pap-smears,” Computational and mathematical methods in medicine, vol. 2014, 2014.
[14] P. W. Poon and J. N. Carter, “Genetic algorithm crossover operators for ordering applications,” Computers & Operations Research, vol. 22, no. 1, pp. 135–147, 1995.
[15] D. M. Deaven and K.-M. Ho, “Molecular geometry optimization with a genetic algorithm,” Physical review letters, vol. 75, no. 2, p. 288, 1995.
[16] T. Calinski and J. Harabasz, “A dendrite method for cluster analysis,” Communications in Statistics-theory and Methods, vol. 3, no. 1, pp. 1–27, 1974.
[17] J. Jantzen, J. Norup, G. Dounias, and B. Bjerregaard, “Pap-smear benchmark data for pattern classification,” Nature inspired Smart Information Systems (NiSIS 2005), pp. 1–9, 2005.
[18] R. A. Fisher, “The use of multiple measurements in taxonomic problems,” Annals of eugenics, vol. 7, no. 2, pp. 179–188, 1936.
[19] S. Petrovic, “A comparison between the silhouette index and the davies-bouldin index in labelling ids clusters,” in Proceedings of the 11th Nordic Workshop of Secure IT Systems. sn, 2006, pp. 53–64.
[20] S. Ding, “Feature selection based f-score and aco algorithm in support vector machine,” in 2009 Second International Symposium on Knowledge Acquisition and Modeling, vol. 1. IEEE, 2009, pp. 19–23.
[21] T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu, “An efficient k-means clustering algorithm: Analysis and implementation,” IEEE Transactions on Pattern Analysis & Machine Intelligence, no. 7, pp. 881–892, 2002.
[22] R. L. Cannon, J. V. Dave, and J. C. Bezdek, “Efficient implementation of the fuzzy c-means clustering algorithms,” IEEE transactions on pattern analysis and machine intelligence, no. 2, pp. 248–255, 1986.
[23] U. Maulik and S. Bandyopadhyay, “Performance evaluation of some clustering algorithms and validity indices,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 12, pp. 1650–1654, 2002.