Abstract
Breast cancer is the second leading cause of cancer deaths in women worldwide and occurs in nearly one out of eight women. Currently there are three techniques to diagnose breast cancer: mammography, FNA (Fine Needle Aspirate) and surgical biopsy. In this paper we develop an integrated expert system for diagnosis, prognosis and prediction for breast cancer using soft computing techniques. The basic aim is to compare the various neural network models from the recent literature. Breast cancer database used for this purpose is from the University of Wisconsin (UCI) Machine Learning Repository. Three different data sets have been used, each employing different diagnostic technique. It can use diagnosis, prognosis and survivability prediction of breast cancer patient in one intelligent system. We implement six models of neural networks namely Back Propagation Algorithm, Radial Basis Function Networks, Learning vector Quantization, Probabilistic Neural Networks, Recurrent Neural Network, and Competitive Neural network. Experimental Results show that different models give optimal performance for different types of data sets. However, all the models are able to solve the problem to a reasonable extent.
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References
Ma, Y., Wang, Z., Lu, J.L., Wang, G., Li, P., Ma, T., Xie, Y., Zheng, Z.: Extracting Micro-calcification Clusters on Mammograms for Early Breast Cancer Detection. In: Proceedings of the 2006 IEEE International Conference on Information Acquisition, Weihai, Shandong, China, August 20-23, pp. 499–504 (2006)
Cheng, H.-D., Lui, Y.M., Freimanis, R.I.: A Novel Approach to Microcalcification Detection Using Fuzzy Logic Technique. IEEE Transactions on Medical Imaging 17(3), 442–450 (1998)
Sameti, M., Ward, R.K., Morgan-Parkes, J., Palcic, B.: Image Feature Extraction in the Last Screening Mammograms Prior to Detection of Breast Cancer. IEEE Journal of Selected Topics in Signal Processing 3(1), 46–52 (2009)
Mumtaz, A.l., Deris, S., Zaki, N., Ghoneim, D.M.: Breast Cancer Detection Based on Statistical Textural Features Classification, pp. 728–730. IEEE, Los Alamitos (2008)
Tan, T.Z., Quek, C., Ng, G.S., Ng, E.Y.K.: A novel cognitive interpretation of breast cancer thermography with complementary learning fuzzy neural memory structure. Expert Systems with Applications 33, 652–666 (2007)
Laufer, S., Rubinsky, B.: Tissue Characterization with an Electrical Spectroscopy SVM Classifier. IEEE Transactions on Biomedical Engineering 56(2), 525–528 (2009)
Santos-Andrk, T.C.S., da Silva, A.C.R.: A Neural Network Made of a Kohonen’s SOM Coupled to a MLP Trained Via Backpropagation for the Diagnosis of Malignant Breast Cancer from Digital Mammograms, pp. 3647–3650. IEEE, Los Alamitos (1999)
Revett, K., Gorunescu, F., Gorunescu, M., El-Darzi, E., Ene, M.: A Breast Cancer Diagnosis System: A Combined Approach Using Rough Sets and Probabilistic Neural Networks. In: EUROCON 2005, Serbia & Montenegro, Belgrade, November 22-24, pp. 1124–1127 (2005)
Xiong, X., Kim, Y., Baek, Y., Rhee, D.W., Kim, S.-H.: Analysis of Breast Cancer Using Data Mining & Statistical Techniques. In: Proceedings of the Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks (SNPD/SAWN 2005). IEEE, Los Alamitos (2005)
Nabil, E., Badr, A., Farag, I., Khozium, M.O.: A Hybrid Artificial Immune Genetic Algorithm with Fuzzy Rules for Breast Cancer Diagnosis. In: INFOS 2008, Cairo-Egypt, March 27-29, pp. 31–40 (2008)
Fogel, D.B., Wasson, E.C., Boughton, E.M., Porto, V.W., Angeline, P.J.: Linear and Neural Models for Classifying Breast Masses. IEEE Transactions on Medical Imaging 17(3), 485–488 (1998)
Davis, S.K., Van Veen, B.D., Hagness, S.C., Kelcz, F.: Breast Tumor Characterization Based on Ultrawideband Microwave Backscatter. IEEE Transactions on Biomedical Engineering 55(1), 237–246 (2008)
Hadjiiski, L., Sahiner, B., Chan, H.-P., Petrick, N., Helvie, M.: Classification of Malignant and Benign Masses Based on Hybrid ART2LDA Approach. IEEE Transactions on Medical Imaging 18(12), 1178–1187 (1999)
Van Ha, K.: Hierarchical Radial Basis Function Networks. In: 1998 EEE, pp. 1893–1898 (1998)
Chen, Y., Wang, Y., Yang, B.: Evolving Hierarchical RBF Neural Networks for Breast Cancer Detection. In: King, I., Wang, J., Chan, L.-W., Wang, D. (eds.) ICONIP 2006, Part II. LNCS, vol. 4234, pp. 137–144. Springer, Heidelberg (2006)
Salleh, N.M., Sakim, H.A.M., Othman, N.H.: Neural Networks to Evaluate Morphological Features for Breast Cells Classification. IJCSNS International Journal of Computer Science and Network Security 8(9), 51–58 (2008)
Salleh, N.M., Sakim, H.A.M., Othman, N.H.: Neural Networks to Evaluate Morphological Features for Breast Cells Classification. IJCSNS International Journal of Computer Science and Network Security 8(9), 51–58 (2008)
Rahmoun, A., Berrani, S.-A.: A Genetic-Based Neuro-Fuzzy Generator: NEFGEN. In: 2001 IEEE, pp. 18-23 (2001)
Pena-Reyes, C.A., Sipper, M.: A fuzzy-genetic approach to breast cancer diagnosis. Artificial Intelligence in Medicine 17, 131–155 (1999)
Peña Reyes, C.A.: Breast Cancer Diagnosis by Fuzzy CoCo. In: Peña Reyes, C.A. (ed.) Coevolutionary Fuzzy Modeling. LNCS, vol. 3204, pp. 71–87. Springer, Heidelberg (2004)
Seker, H., Odeyato, M., Petrovie, D., Naguib, R.N.G., Bartoli, C., Alasilo, L., Sherbet, G.V.: Prognostic comparison of statistical, neural and fuzzy methods of analysis of breast cancer image cytometric data. In: 2001 Proc. of the 23rd Annual IEEE MBS Int. Conference, Istanbul, Turkey, October 25-28, pp. 3811–3814 (2001)
Anupam, S., Ritu, T., Janghel, R.R., Prabhdeep, K.: Diagnosis of Thyroid Disorders using Artificial Neural Networks on 2009 IEEE International Advance Computing Conference (IACC 2009), Patiala, India, March 6-7, pp. 2722–2726 (2009)
Sivanandan, S.N., Deepa, S.N.: Principle of Soft Computing. Wiley India Private Limited, Chichester (2007)
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Janghel, R.R., Shukla, A., Tiwari, R., Kala, R. (2010). Intelligent Decision Support System for Breast Cancer. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6146. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13498-2_46
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DOI: https://doi.org/10.1007/978-3-642-13498-2_46
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