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

2008 ◽  
Author(s):  
Tadashi Kondo
Keyword(s):  
Neural Network ◽  
Image Recognition ◽  
Prediction Error ◽  
Medical Image ◽  
Error Criterion ◽  
3 Dimensional ◽  
Medical Image Recognition

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

2007 ◽  
Vol 11 (1) ◽  
pp. 96-104 ◽  
Author(s):  
Tadashi Kondo ◽  
◽  
Junji Ueno ◽  
Abhijit S. Pandya ◽  
Keyword(s):  
Neural Network ◽  
Image Recognition ◽  
Network Architecture ◽  
Prediction Error ◽  
Medical Image ◽  
Basis Functions ◽  
Rbf Network ◽  
3 Dimensional ◽  
Medical Image Recognition ◽  
Neural Network Algorithm

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.

Revised GMDH-Type Neural Networks Using AIC or PSS Criterion and Their Application to Medical Image Recognition

2005 ◽  
Vol 9 (3) ◽  
pp. 257-267 ◽  
Author(s):  
Tadashi Kondo ◽  
◽  
Junji Ueno ◽  
Kazuya Kondo ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Recognition ◽  
Prediction Error ◽  
Medical Image ◽  
Group Method ◽  
Error Criterion ◽  
Network Algorithm ◽  
Medical Image Recognition ◽  
Neural Network Algorithm

This study deals with the revised Group Method of Data Handling (GMDH)-type neural network algorithm using prediction error criterion defined as Prediction Sum of Squares (PSS) or Akaike's Information Criterion (AIC). The revised GMDH-type neural network algorithm generates optimum multilayered neural network architectures fitting the complexity of nonlinear systems using heuristic self-organization. The revised GMDH-type neural networks self-select the number of layers, optimum neuronal architectures, and useful input variables to minimize prediction error criterion defined as PSS or AIC. This algorithm is applied to the identification problem of the nonlinear complex system and results are compared to those obtained by the revised GMDH algorithm and conventional multilayered neural networks. The revised GMDH-type neural network algorithm is also applied to medical image recognition and it is shown that this algorithm is useful for medical image recognition.

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