A Radial Basis Function Neural Network (RBFNN) Approach for Structural Classification of Thyroid Diseases
- 221 Downloads
The thyroid is a gland that controls key functions of body. Diseases of the thyroid gland can adversely affect nearly every organ in human body. The correct diagnosis of a patient’s thyroid disease clarifies the choice of drug treatment and also allows an accurate assessment of prognosis in many cases. This study investigates Multilayer Perceptron Neural Network (MLPNN) and Radial Basis Function Neural Network (RBFNN) for structural classification of thyroid diseases. A data set for 487 patients having thyroid disease is used to build, train and test the corresponding neural networks. The structural classification of this data set was performed by two expert physicians before the input variables and results were fed into the neural networks. Experimental results show that the predictions of both neural network models are very satisfying for learning data sets. Regarding the evaluation data, the trained RBFNN model outperforms the corresponding MLPNN model. This study demonstrates the strong utility of an artificial neural network model for structural classification of thyroid diseases.
KeywordsRadial Basis Function Neural Network (RBFNN) Multilayer Perceptron Neural Network (MLPNN) Levenberg-Marquardt Structural classification Thyroid
The authors would like to express their respect to the memory of late Dr. Mustafa Kocak who had given the inspiration of this study and thank to Cukurova University Research Hospital personnel for providing medical data for this study.
- 1.Werner, S. C., and Ingbar, S. H., Diseases of the thyroid. In: Werner, S. C., Ingbar S. H., et al., (Eds.), The thyroid: A fundamental and clinical text. 4th Ed. New York: Harper and Row, 1978, pp. 389–393.Google Scholar
- 3.Braverman, L.E., and Utiger, R.D. (Eds.), The thyroid: a fundamental and clinical text, 8th Ed. Philadelphia, Lippincot Williams & Wilkins, 2000, pp. 515–719.Google Scholar
- 5.Grünwald, F.B., Thyroid disease. In: Ell, P.J., and Gambhir, S.S., (Eds.), Nuclear medicine in clinical diagnosis and treatment. New York: Churchill Livingstone, pp. 383–392, 2004.Google Scholar
- 6.Feld, S., et al., AACE Clinical guidelines for the diagnosis and management of thyroid nodules. Endocr. Pract. 2(1):78–84, 1996.Google Scholar
- 16.Sharpe, P. K., Solberg, H. E., Rootwelt, K., and Yearworth, M., Artificial neural networks in diagnosis of thyroid function from vitro laboratory tests. Clin. Chem. 39:2248–2253, 1993.Google Scholar
- 18.Ping, W. L., Phuan, A. T. L., and Jian, X., Hierarchical fast learning artificial neural network: progressive learning in high dimensional spaces. International Report, 2004.Google Scholar
- 19.Zhang, H., and Lin, F. C., Medical diagnosis by virtual physician. 12th IEEE Symposium on Computer-Based Medical Systems, 1999.Google Scholar
- 20.Krose, B., and Smaget, P. V. D., An introduction to neural networks. Amsterdam, The University of Amsterdam Press, 1996.Google Scholar
- 22.SAS Institute Inc., ftp://ftp.sas.com/pub/neural/FAQ2.html, 2002.
- 23.Duda, R. O., Hart, P. E., and Stork, D. G., Pattern classification. New York, Wiley, 2000.Google Scholar
- 24.Bernand, E., Optimization training neural nets. IEEE Trans. Neural Netw. 3:2989–993, 1992.Google Scholar
- 26.Fontenla-Romero, O., Erdogmus, D., Principe, J. C., Alonso-Betanzos, A., and Castillo, E., Accelerating the converge speed of neural networks learning methods using least squares. European Symposium on Artificial Neural Networks, 2003, pp. 255–260.Google Scholar
- 27.Wilamowki, B. M., Iqlikci, S., Kaynak, O., and Onder, E. M., An algorithm for fast converge in training neural networks. IEEE Proceedings of International Joint Conference on Neural Networks, pp. 1778–1782, 2005.Google Scholar
- 29.Manolis, I. A. L., and Antonis, A. A., Is Levenberg–Marquardt the most efficient optimization algorithm for implementing bundle adjustment. IEEE Proceedings of International Conference on Computer Vision 2:1526–1531, 2005.Google Scholar
- 30.Lee, C, Chung, P, Tsai, J, and Chang, C, Robust radial basis function neural networks. IEEE Transactions on Systems, Man, and Cybernetics—B: Cybernetics 29:674–685, 1999.Google Scholar