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Frontiers in Signal Processing
FSP > Volume 4, Number 2, April 2020

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 10, 2020
DOI: 10.22606/fsp.2020.42001

Author(s)
M. Kerem Un, Mustafa Guven, Caglar Cengizler, Seyda Erdogan, Aysun Uguz
Affiliation(s)
Faculty of Engineering and Architecture, Department of Biomedical Engineering, Cukurova University, Adana 01330, Balcali, Turkey
Faculty of Engineering and Architecture, Department of Biomedical Engineering, Cukurova University, Adana 01330, Balcali, Turkey
Faculty of Engineering and Architecture, Department of Biomedical Engineering, Cukurova University, Adana 01330, Balcali, Turkey
Faculty of Medicine, Department of Pathology, Cukurova University, Adana 01330, Balcali, Turkey
Faculty of Medicine, Department of Pathology, Cukurova University, Adana 01330, Balcali, Turkey
Abstract
This paper presents an utilization of data clustering with genetic algorithm (GA) approach. Proposed meta-heuristic clustering approach relies on genetic operators and accepts Calinski-Harabasz (CH) measure as fitness criteria where each individual represents a final judgement about existence of malignancy on set of cervical cells. It was aimed to evaluate the performance of fitness criteria on detection of malignancy where classification is performed on salient morphological features. Preferred fitness criteria measures the ability of individuals in a population to form appropriate clusters for normal and abnormal cell samples. Feature space includes data extracted from the previously segmented cervical cell images. Proposed approach is examined with two data sets which contains malignant and healthy cell samples. Preliminary results has shown that preferred fitness criteria for the classification is promising and the presented utilization of GA based clustering approach with CH criteria has a better clustering performance compared to conventional clustering methods.
Keywords
Calinski-Harabasz; Clustering; Cervical Cancer; Meta-Heuristic; Genetic Algorithm
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.
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