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JACIII Vol.11 No.1 pp. 96-104
doi: 10.20965/jaciii.2007.p0096
(2007)

Paper:

Multilayered GMDH-Type Neural Network with Radial Basis Functions and its Application to 3-Dimensional Medical Image Recognition of the Liver

Tadashi Kondo*, Junji Ueno*, and Abhijit S. Pandya**

*School of Health Sciences, The University of Tokushima, 3-18-15 Kuramoto-cho, Tokushima 770-8509, Japan

**Computer Sciences & Engineering, Florida Atlantic University, Boca Raton, FL 33431, U.S.A.

Received:
March 20, 2006
Accepted:
June 9, 2006
Published:
January 20, 2007
Keywords:
GMDH, neural network, medical image recognition, RBF
Abstract

In this paper, a Group Method of Data Handling (GMDH)-type neural network algorithm with radial basis functions (RBF) is proposed. The proposed algorithm generates optimum RBF network architectures fitting the complexity of nonlinear systems using heuristic self-organization. The number of hidden layers, the number of neurons in hidden layers and relevant input variables are selected by minimizing prediction error defined as Akaike’s Information Criterion (AIC). Various nonlinear combinations of variables are initially generated in each layer and only relevant combinations are selected based on AIC. Hence, the optimum RBF network architecture fitting the complexity of the nonlinear system is obtained. We apply the GMDH-type neural network algorithm with RBF to 3-dimensional medical image recognition of the liver, showing that this algorithm is very easy and useful in 3-dimensional medical image recognition of the liver because the neural network architecture is automatically organized to minimize prediction error based on AIC.

Cite this article as:
Tadashi Kondo, Junji Ueno, and Abhijit S. Pandya, “Multilayered GMDH-Type Neural Network with Radial Basis Functions and its Application to 3-Dimensional Medical Image Recognition of the Liver,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.1, pp. 96-104, 2007.
Data files:
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Last updated on Oct. 21, 2021