CANFIS—a computer aided diagnostic tool for cancer detection

Latha Parthiban, R. Subramanian

Department of Computer Science, Pondicherry University, Puducherry, India..
Research Scholar in Pondicherry University and working in Aarupadai Veedu Institute of Technology, Chennai, India.

DOI: 10.4236/jbise.2009.25048                

Abstract

In this investigation, an approach using Coac-tive Neuro-Fuzzy Inference System (CANFIS) as diagnosis system for breast cancer has been proposed on Wisconsin Breast Cancer Data (WBCD). It is occasionally difficult to attain the ultimate diagnosis even for medical experts due to the complexity and non-linearity of the rela-tionships between the large measured factors, which can be possibly resolved with a human like decision-making process using Artificial Intelligence (AI) algorithms. CANFIS is an AI algorithm which has the advantages of both fuzzy inference system and neural networks and can deal with ambiguous data and learn from the past data by itself. The Multi Layer Percep-tron Neural Network (MLPNN), Probabilistic Neural Network (PNN) Principal Component Analysis (PCA), Support Vector Machine (SVM) and Self Organizing Map (SOM) were also tested and benchmarked for their p

Keywords

Neural Network; Coactive Neuro-Fuzzy Inference Systems; Probabilistic Neural Network; Principal Component Analysis; Stern Series; Wisconsin Breast Cancer Data

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Ioanna, C., Evalgelos, D., and George, K., (2000) Fast detec-tion of masses in computer aided mammography, IEEE Signal Processing Magazine: January, 54–64.
[2]	Pena-Reyes, C. A. and Sipper, M., (91999) A fuzzy-genetic approach to breast cancer diagnosis, Artificial Intelligence in Medicine, 131–155.
[3]	Pena-Reyes, C. A. and Sipper, M., (1998) Evolving fuzzy rules for breast cancer diagnosis, In Proceedings of 1998 Interna-tional Symposium on Nonlinear Theory and Applications.
[4]	Wang, J.-S. and Lee, G. C. S., (2002) Self-adaptive neu-ron-fuzzy inference systems for classification applications, IEEE Transactions on Fuzzy Systems.
[5]	Arulampalam, G. and Bouzerdoum, A., (2001) Application of shunting inhibitory artificial neural networks to medical diag-nosis, Seventh Australian and New Zealand Intelligent Infor-mation Systems Conference.
[6]	Land, W. H., Jr., Masters, T., and Lo, J. Y., (2000) Application of a new evolutionary programming/adaptive boosting hybrid to breast cancer diagnosis, IEEE Congress on Evolutionary Computation Proceedings.
[7]	Meesad, P. and Yen, G. G., (2003) Combined numerical and linguistic knowledge representation and its application to medical diagnosis, IEEE Transactions on Systems, Man, and Cybernatics.
[8]	Jang, J.-S. R., (1993) ANFIS: Adaptive-network based fuzzy inference system., IEEE Trans. on System, Man and Cybernet-ics, 23(3).
[9]	Jang, J. R. and Sun, C. T. (1995) Neuro-fuzzy modeling and control, Proceedings of the IEEE.
[10]	Freund, Y. and Schapire, R. E., (1996) Experiments with a new boosting algorithm, Machine Learning: Proceedings of the Thirteenth International Conference.
[11]	Schapire, R. E., (1999) Theoretical views of boosting and ap-plications, Proceedings of Algorithmic Learning Theory.
[12]	Sebri, A., Malek, J., and Tourki, R., (2007) Automated breast cancer diagonosis based on GVF-snake segmentation, wavelet features extraction and neural network classification, J. Com-puter Science, 3(8), 600–607.
[13]	Goldberg, D., (1989) Genetic algorithm in search, optimization, and machine learning, Addison-Wesley.
[14]	John, G. H., Kohavi, R., and Pfleger, K., (1994) Irrelevant fea-tures and the subset selection problem, Machine Learning: Proceedings of the Eleventh International Conference.
[15]	Mangasarian, O. L., Street, W. N., and Wolberg, W. H., (1994) Breast cancer diagnosis and prognosis via linear programming, Mathematical Programming Technical Report 9410, University of Wisconsin.
[16]	Song, H.-J., Lee, S.-G., and Park, G.-T., (2005) A Methodology of computer aided diagnostic system on breast cancer, Pro-ceedings of the IEEE Conference on Control Applications To-ronto, Canada.
[17]	Haykin, S., (1994) Neural networks: A comprehensive founda-tion, NewYork: Macmillan.
[18]	Chaudhuri, B. B. and Bhattacharya, U., (2000) Efficient train-ing and improved performance of multilayer perceptron in pattern classification, Neurocomputing, 34, 11– 27.
[19]	Hagan, M. T. and Menhaj, M. B., (1994) Training feedforward networks with the Marquardt algorithm, IEEE Trans. Neural Netw., 5(6), 989–993.
[20]	Specht, D. F., (1990) Probabilistic neural networks, Neural Netw., 3(1), 109–118.
[21]	Burrascano, P., (1991) Learning vector quantization for the probabilistic neural network, IEEE Trans. Neural Netw., 2(4), 458–461.
[22]	Vapnik, V., (1995) The Nature of statistical learning theory, NewYork: Springer-Verlag.
[23]	Cortes and Vapnik, (1995) Support vector networks, Mach. Learn., 20(3), 273–297.
[24]	Setiono, R., (2000) Generating concise and accurate classifica-tion rules for breast cancer diagnosis, Artificial Intelligence in Medicine.
[25]	American Cancer Society Hompage, (20 July 2008) Citing Internet sources URL: http://www.cancer.org.
[26]	Johnson, M. B., (2003) Sterns diatomic array applied to fibo-nacci representations, Fibonacci Quarterly 41, 169– 180.