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Neural Computing and Applications

Erschienen in:

01.06.2013 | Original Article

Local linear wavelet neural network based breast tumor classification using firefly algorithm

verfasst von: M. R. Senapati, P. K. Dash

Erschienen in: Neural Computing and Applications | Ausgabe 7-8/2013

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Abstract

Breast cancer is the major cause of cancer deaths in women today and it is the most common type of cancer in women. This paper presents some experiments for classifying breast cancer tumor and proposes the use of firefly algorithm (FA) to improve the performance of Local linear wavelet neural network. This work in fact uses FA to optimize the parameters of local linear wavelet neural network. The experiments were conducted on extracted breast cancer data from University of Winconsin Hospital, Madison. The result has been compared with a wide range of classifiers to evaluate its performance. The evaluations show that the proposed approach is very robust, effective and gives better correct classification as compared to other classifiers.

Vorheriger Artikel Training of an artificial neural network in the diagnostic system of a technical object

Nächster Artikel Nonlinear discriminant clustering based on spectral regularization

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http://ailab.si/orange/doc/datasets/breast-cancer-wisconsin-cont.html.

Antonie M-L, Zaine OR, Coman A (2001) Application of data mining techniques for medical image classification. In: Proceedings in the 2nd international workshop on multimedia data mining, (MDM/KDD’2001), San Francisco, USA, August 26th, pp 94–101

Lai S, Li X, Bischof W (1989) On techniques for detecting circumscribed masses in mammograms. IEEE Trans Med Imaging 8(4):377–386CrossRef

Liu B, Cheng HD, Huang J, Tian J, Tang X, Lie J (2010) Fully Automatic and segmentation—robust classification of breast tumors based on local texture analysis of ultra sound images. Pattern Recognit 43:280–298MATHCrossRef

Cheng HD, Shan J, Ju W, Guo Y, Jhang L (2010) Automated breast cancer detection and classification using ultra sound images, a survey. Pattern Recognit 43:299–317MATHCrossRef

Wang T, Narayannis N (1998) Detection of micro calcification in digital mammograms using Wavelets. IEEE Trans Med Imaging 17(4):498–509CrossRef

Christoyianni I et al (2000) Fast detection of masses in computer-aided mammography. IEEE Signal Process Mag 54–64

Chen C, Lee G (1997) Image segmentation using multi resolution wavelet analysis and expectation maximization (em) algorithm for digital mammography. Int J Image Syst Technol 8(5):491–504CrossRef

Zou W, Chi Z, Lo KC (2008) Improvement of image classification using wavelet coefficients with structured-based neural network. Int J Neural Syst 18(3):195–205. doi:10.1142/S012906570800152x CrossRef

Li H et al (1997) Fractal modeling and segmentation for the enhancement of micro calcification in digital mammograms. IEEE Trans Med Imaging 16(6):785–798CrossRef

10.

Chan H et al (1998) Computerized analysis of mammographic micro calcification in morphological and feature spaces. Med Phys 25(10):2007–2019CrossRef

11.

Downs J, Harrison RF, Cross SS (1998) A decision support tool for the diagnosis of breast cancer based upon Fuzzy ARTMAP. Neural Comput Appl 7(2):147–165. doi:10.1007/BF01414167

12.

Li H et al (1995) Markov random field for tumor detection in digital mammography. IEEE Trans Med Imaging 14(3):565–576CrossRef

13.

Ripley RM, Harris AL, Tarassenko L (1998) Neural network models for breast cancer prognosis. Neural Comput Appl 7(4):367–375. doi:10.1007/BF01428127

14.

Huang Y-L, Wang K-L, Chen D-R (2006) Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines. Neural Comput Appl 15(2):164–169. doi:10.1007/s00521-005-0019-5

15.

Chen Y, Yang B, Dong J (2006) Time series prediction using a local linear wavelet neural network. Neuro Comput 69:449–465

16.

Chen Y, Dong J, Yang B, Zhang Y (2004) Local linear wavelet neural network. Fifth world congress on intelligent control and automation (WCIA), Hangzhou

17.

Yang XS (2008) Nature-inspired meta heuristic algorithms. Luniver Press

18.

El-Sebakhy E (2004) Functional networks as a new frame work for the pattern classification problems. Ph.D Thesis, Cornell University, USA

19.

El-Sebakhy EA, Faisal KA, Helmy T, Azzedin F, Al-Suhaim A (2006) Evaluation of breast cancer tumor classification with unconstrained functional networks classifier. IEEE Int Conf Comput Syst Appl 281–287. doi:10.1109/AICCSA.2006.205102

20.

Karabatak M, Ince MC (2009) An expert system for detection of breast cancer based on association rules and neural network. Expert Syst Appl 36((2, Part 2)):3465–3469CrossRef

21.

Peña-Reyes CA, Sipper M (1999) A fuzzy-genetic approach to breast cancer diagnosis. Artif Intell Med 17(2):131–155CrossRef

22.

Abonyi J, Szeifert F (2003) Supervised fuzzy clustering for the identification of fuzzy classifiers. Pattern Recognit Lett 24(14):2195–2207MATHCrossRef

23.

Guijarro-Berdiñas B, Fontenla-Romero O, Pérez-Sánchez B, Praguela F (2007) A linear learning method for multilayer perceptrons using least-squares. In: Intelligent data engineering and automated learning—IDEAL, vol 4881. Lecture notes in computer science. Springer, Berlin, pp 365–374. doi:10.1007/978-3-540-77226-2_38

24.

Senapati MR, Mohanty A, Dash S, Dash PK (2011) Local linear wavelet neural network for breast cancer recognition. Neural Comput Appl. doi:10.1007/s00521-011-0670-y

25.

Mat A, Harsa S, Nuryanti MS, Nor HO (2009) Neural network inputs selection for breast cancer cells classification, studies in computational intelligence, vol 199, Springer, pp 1–11. doi:10.1007/978-3-642-00909-9_1

26.

Ster B, Dobnikar A (1996) Neural network in medical diagnosis: comparison with other methods, EANN’96, pp 427–430

27.

Kordos M, Blachnik M, Strzempa D (2010) Do we need more than k-NN? Artif Intell Soft Comput Lect Notes Soft Comput 6113:414–421. doi:10.1007/978-3-642-13208-7_52 CrossRef

Titel: Local linear wavelet neural network based breast tumor classification using firefly algorithm
verfasst von: M. R. Senapati
P. K. Dash
Publikationsdatum: 01.06.2013
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 7-8/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-0927-0

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