An Overview of the Application of Fuzzy Inference System for the Automation of Breast Cancer Grading with Spectral Data
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An Overview of the Application of Fuzzy Inference System for the Automation of Breast Cancer Grading with Spectral Data

Authors: Shabbar Naqvi, Jonathan M. Garibaldi

Abstract:

Breast cancer is one of the most frequent occurring cancers in women throughout the world including U.K. The grading of this cancer plays a vital role in the prognosis of the disease. In this paper we present an overview of the use of advanced computational method of fuzzy inference system as a tool for the automation of breast cancer grading. A new spectral data set obtained from Fourier Transform Infrared Spectroscopy (FTIR) of cancer patients has been used for this study. The future work outlines the potential areas of fuzzy systems that can be used for the automation of breast cancer grading.

Keywords: Breast cancer, FTIR, fuzzy inference system, principal component analysis

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1083531

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References:


[1] A. C. Society, "Cancer Facts and Figures " 2012.
[2] J. Backhaus, R. Mueller, N. Formanski, N. Szlama, H.-G. Meerpohl, M. Eidt, and P. Bugert, "Diagnosis of breast cancer with infrared spectroscopy from serum samples," Vibrational Spectroscopy, vol. 52, pp. 173-177, 2010.
[3] X. Y. Wang, "Fuzzy Clustering in the Analysis of Fourier Transform Infrared Spectra for Cancer Diagnosis," in School of Computer Science. PhD: University of Nottingham, 2006.
[4] S. Naqvi and J. M. Garibaldi, "The complexities involved in the analysis of Fourier Transform Infrared Spectroscopy of breast cancer data with clustering algorithms," in Computer Science and Electronic Engineering Conference (CEEC), 2011 3rd, 2011, pp. 80-85.
[5] S. Naqvi and J. Garibaldi, "An Investigation into the use of Fuzzy CMeans Clustering of Fourier Transform Infrared Microscopic Data for the Automation of Breast Cancer Grading," in Proceedings of the 9th Annual Workshop on Computational Intelligence (UKCI 2009) Nottingham, UK 2009.
[6] E. A. Rakha, M. E. El-Sayed, A. H. S. Lee, C. W. Elston, M. J. Grainge, Z. Hodi, R. W. Blamey, and I. O. Ellis, "Prognostic Significance of Nottingham Histologic Grade in Invasive Breast Carcinoma," J Clin Oncol, vol. 26, pp. 3153-3158, July 1, 2008 2008.
[7] S. Naik, S. Doyle, S. Agner, A. Madabhushi, M. Feldman, and J. Tomaszewski, "Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology," in Biomedical Imaging: From Nano to Macro, ISBI 2008. , 2008, pp. 284-287.
[8] S. N. Sivanandam, S. Sumathi, and S. N. Deepa, Introduction to Fuzzy Logic using MATLAB vol. 15: Springer, 2007.
[9] H. Hamdan and J. M. Garibaldi, "Adaptive neuro-fuzzy inference system (ANFIS) in modelling breast cancer survival," in Fuzzy Systems (FUZZ), 2010 IEEE International Conference on, pp. 1-8.
[10] M. Castanys, R. Perez-Pueyo, M. J. Soneira, E. Golobardes, and A. Fornells, "Identification of Raman spectra through a case-based reasoning system: application to artistic pigments," Journal of Raman Spectroscopy, vol. 42, pp. 1553-1561, 2011.
[11] A. G. Evsukoff, A. C. S. Branco, and S. Galichet, "Intelligent data analysis and model interpretation with spectral analysis fuzzy symbolic modeling," International Journal of Approximate Reasoning, vol. 52, pp. 728-750, 2011.
[12] C. Cernuda, E. Lughofer, W. Märzinger, and J. r. Kasberger, "NIRbased quantification of process parameters in polyetheracrylat (PEA) production using flexible non-linear fuzzy systems," Chemometrics and Intelligent Laboratory Systems, vol. 109, pp. 22-33, 2011.
[13] S. Z. Mahmoodabadi, J. Alirezaie, P. Babyn, A. Kassner, and E. Widjaja, "Wavelets and fuzzy relational classifiers: A novel spectroscopy analysis system for pediatric metabolic brain diseases," Fuzzy Sets and Systems, vol. 161, pp. 75-95, 2009.
[14] Y. Zhengmao, "Artificial-intelligence approach for biomedical sample characterization using Raman spectroscopy," Automation Science and Engineering, IEEE Transactions on, vol. 2, pp. 67-73, 2005.
[15] R. Perez-Pueyo, M. J. Soneira, and S. Ruiz-Moreno, "A fuzzy logic system for band detection in Raman spectroscopy," Journal of Raman Spectroscopy, vol. 35, pp. 808-812, 2004.
[16] S. G. Kong, Y.-R. Chen, I. Kim, and M. S. Kim, "Analysis of Hyperspectral Fluorescence Images for Poultry Skin Tumor Inspection," Appl. Opt., vol. 43, pp. 824-833, 2004.
[17] "Bio Max Website," 2012(www.biomax.us)
[18] A. Hyvärinen, "Survey on Independent Component Analysis," Neural Computing Surveys, vol. 2, pp. 94-128, 1999.