An Overview of the Application of Fuzzy Inference System for the Automation of Breast Cancer Grading with Spectral Data
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.
[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.
[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.
@article{"International Journal of Medical, Medicine and Health Sciences:63736", author = "Shabbar Naqvi and Jonathan M. Garibaldi", title = "An Overview of the Application of Fuzzy Inference System for the Automation of Breast Cancer Grading with Spectral Data", 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", volume = "6", number = "4", pages = "103-5", }
