Feedback Gmdh-type Neural Network and Its Application to Medical Image Analysis of Liver Cancer

A feedback Group Method of Data Handling (GMDH)-type neural network algorithm is proposed, and is applied to nonlinear system identification and medical image analysis of liver cancer. In this feedback GMDH-type neural network algorithm, the optimum neural network architecture is automatically selected from three types of neural network architectures, such as sigmoid function neural network, radial basis function (RBF) neural network, and polynomial neural network. Furthermore, the structural parameters, such as the number of feedback loops, the number of neurons in the hidden layers, and the relevant input variables, are automatically selected so as to minimize the prediction error criterion defined as Akaike’s Information Criterion (AIC) or Prediction Sum of Squares (PSS). The feedback GMDH-type neural network has a feedback loop and the complexity of the neural network increases gradually using feedback loop calculations so as to fit the complexity of the nonlinear system. The results of the feedback GMDH-type neural network are compared to those obtained by GMDH and conventional neural network trained using the back propagation algorithm. It is shown that the feedback GMDH-type neural network algorithm is accurate and a useful method for the nonlinear system identification and the medical image analysis of liver cancer, and is ideal for practical complex problems since the optimum neural network architecture is automatically organized.