Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

This is an Open Access publication published under CSC-OpenAccess Policy.
Performance Evaluation of Neural Classifiers Through Confusion Matrices To Diagnose Skin Conditions
Ihsan Omur BUCAK
Pages - 15 - 27     |    Revised - 24-02-2014     |    Published - 19-03-2014
Volume - 5   Issue - 2    |    Publication Date - March 2014  Table of Contents
Artificial Intelligence, Artificial Neural Networks, Medical Diagnosing, Neural Classifiers, Skin Conditions/Diseases, Confusion Matrix, F-score.
In this paper we have aimed to diagnose skin conditions using Artificial Intelligence (AI) based classifier algorithms and do the performance analyses of those presented algorithms through confusion matrices. These algorithms are being used in a large array of different areas including medicine, and display very distinct characteristics in the sense that they are grouped under different categories such as supervised, unsupervised, statistical, or optimization. The objective of this study is to diagnose skin conditions using seven different well-known and popular as well as emerging Artificial Intelligence based algorithms and to help general practitioners and/or dermatologists develop a careful and supportive approach that leads to a probable diagnosis of skin conditions or diseases. These algorithms we chose as neural classifiers include Back- Propagation (BP), Random Forest (RF), Support Vector Machines (SVMs), Linear Vector Quantization (LVQ), Self-Organizing Maps (SOMs), Naïve Bayes, and finally Bayesian Networks. All of these algorithms have been tested and their results of diagnosing skin conditions/diseases by using data set from Dermatology Database have been compared.
CITED BY (1)  
1 Karlik, B. The Positive Effects of Fuzzy C-Means Clustering on Supervised Learning Classifiers.
1 Google Scholar
2 CiteSeerX
3 refSeek
4 Scribd
5 SlideShare
6 PdfSR
1 M. Antkowiak, Artificial Neural Networks vs. Support Vector Machines for Skin Diseases Recognition, Msc. Thesis, Umea University, Sweden, 2006.
2 http://www.skincancer.org/Skin-Cancer-Facts/, accessed May 27, 2010.
3 A. Frank and A. Asuncion, UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science, 2010.
4 S. Russell and P. Norvig, “Artificial Intelligence: A Modern Approach”, 3rd edition, Prentice Hall, p.578, 2009.
5 C.M. Bishop, “Neural networks and their applications”, Review of Scientific Instruments, 65(6),pp. 1803–1832, 1994.
6 C.M. Bishop, “Neural Networks for Pattern Recognition”, Clarendon Press, Oxford, UK, 1995.
7 B.D. Ripley, “Pattern Recognition and Neural Networks”, Cambridge University Press,Cambridge, UK, 1996.
8 C. Stergiou and D. Siganos, “Neural networks”, SURPRISE 96 Journal, 4, 1996.
9 R. Tadeusiewicz, “Sieci neuronowe, (Neural Networks)”, Akademicka Oficyna Wydawnicza,Warszawa, 1993.
10 M. Antkowiak, “Recognition of skin diseases using artificial neural networks”, In Proceeding of USCCS’05, pp. 313–325, 2005.
11 A.E. Bryson and Y. Ho, “Applied optimal control: optimization, estimation, and control”,Blaisdell Publishing Company or Xerox College Publishing, pp. 481, 1969.
12 D.E. Rumelhart, G.E. Hinton, and R.I. Williams, “Learning representations by backpropagating errors”, Nature, 323, pp. 533-536, 1986.
13 E. Sivasankar, R.S. Rajesh, and S.R.Venkateswaran, “Diagnosing Appendicitis Using Backpropagation Neural Network and Bayesian Based Classifier”, International Journal of Computer Theory and Engineering, Vol. 1, No. 4, pp. 1793-8201, 2009.
14 T. Kohonen, “Self-Organizing Maps”, Springer, Berlin, 1997.
15 T. Kohonen, G. Barna, and R. Chrisley, “Statistical pattern recognition with neural networks:Benchmarking studies”, In Proceedings of the International Conference on Neural Networks(ICNN), Vol. I, pp. 61-68, 1988.
16 T. Kohonen, Learning vector quantization. In: M.A. Arbib, editor, The Handbook of Brain Theory and Neural Networks, pages 537–540. MIT Press, Cambridge, MA, 1995.
17 http://www.nd.com/models/sofm.htm, Self-Organizing Feature Maps, accessed April 5, 2011.
18 http://en.wikipedia.org/wiki/Self-organizing_map, Self-organizing map, accessed April 5,2011.
19 T. Kohonen and T. Honkela, “Kohonen network”, Scholarpedia 2007,http://www.scholarpedia.org/article/Kohonen_network, accessed May 25, 2010.
20 S. Haykin, “9. Self-organizing maps”, Neural networks - A comprehensive foundation (2nd ed.), Prentice-Hall, 1999.
21 N. Cristianini, “An Introduction to Support Vector Machines and Other Kernel-based Learning Methods”, Cambridge University Press, 2000.
22 B. Schlkopf and A.J. Smola, “Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond”, MIT Press, 2001.
23 L. Breiman, "Random Forests". Machine Learning 45 (1): 5–32, 2001.
24 T.K. Ho, "Random Decision Forest". Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, 14–16 August 1995. pp. 278–282, 1995.
25 T.K. Ho, "The Random Subspace Method for Constructing Decision Forests". IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (8): 832–844, 1998.
26 M. Kantardzic. Data Mining - Concepts, Models, Methods, and Algorithms, IEEE Press, Wiley-Interscience, 2003.
27 S. Theodoridis and K. Koutroumbas. Pattern Recognition. San Diego: Academic Press,2006.
28 T.M. Mitchell. Machine Learning. McGraw-Hill, 1997.
29 J. Pearl, Causality: Models, Reasoning, and Inference, Cambridge University Press, 2000.
30 R.E. Neapolitan. Learning Bayesian networks. Prentice Hall, 2004.
31 N. Friedman, M. Linial, I. Nachman, and D. Pe'er, "Using Bayesian Networks to Analyze Expression Data", Journal of Computational Biology (Larchmont, New York: Mary Ann Liebert, Inc.) 7 (3/4): 601–620, 2000.
32 http://www.thefreedictionary.com/differential+diagnosis, accessed October 7, 2013.
33 G. Demiröz, H. A. Güvenir, and N. Ilter, “Learning differential diagnosis of eryhematosquamous diseases using voting feature intervals”, Artificial Intelligence in Medicine, 13, pp.147-165, 1998.
34 I. Ö. Bucak, Y. Sahin, and A. Serbetci, “Supervised vs. Unsupervised neural classifiers for diagnosing skin diseases”, The Proceedings of Second International Conference on Informatics (ICI’2011), Çanakkale-Turkiye, pp. 73-80, 27-29 April 2011.
35 MATLAB R2008b, Natick, Massachusetts: The MathWorks Inc., 2008.
36 K.K Manjusha, K. Sankaranarayanan, and P. Seena, “Prediction of Different Dermatological Conditions Using naïve Bayesian Classification”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 1, pp. 864-868, 2014.
37 R. Blanco, I. Inza, M. Merino, J. Quiroga, and P. Larranaga, “Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS”, Journal of Biomedical Informatics, Volume 38, Issue 5, pp. 376-388, 2005.
38 S. Shirali-Shahreza, M.E. Mousavi, “A New Bayesian Classifier for Skin Detection”, 3rd International Conference on Innovation Computing and Control, pp. 172-175, 2008.
39 A. Masood, A.A. Al-Jumaily, “Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms”, International Journal of Biomedical Imaging, Volume 2013, 22 pages, 2013.
Associate Professor Ihsan Omur BUCAK
Melikºah University - Turkey