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Feedback GMDH-type neural network using prediction error criterion and its application to 3-dimensional medical image recognition

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1 Author(s)
Tadashi Kondo ; School of Health Sciences, the University of Tokushima, 3-18-15 Kuramoto-cho, 770-8509, Japan

The feedback group method of data handling (GMDH)-type neural network algorithm proposed in this paper is applied to 3-dimensional medical image recognition of the brain. The neural network architecture fitting the complexity of the medical images is automatically organized so as to minimize the prediction error criterion defined as Akaikepsilas information criterion (AIC) or prediction sum of squares (PSS). In this algorithm, the optimum neural network architecture is automatically selected from three types of neural network architectures such as the sigmoid function type neural network, the radial basis function (RBF) type neural network and the polynomial type neural network. The recognition results show that the feedback GMDH-type neural network algorithm is useful for the 3-dimensional medical image recognition of the brain and is very easy to apply the practical complex problem because the optimum neural network architecture is automatically organized.

Published in:

SICE Annual Conference, 2008

Date of Conference:

20-22 Aug. 2008