Feedback GMDH-type Neural Network Self-Selecting Various Functions and Its Application to Medical Image Diagnosis of Lung Cancer

Abstract

The feedback Group Method of Data Handling (GMDH) -type neural network algorithm is applied to the medical image diagnosis of lung cancer. In this feedback GMDH-type neural network algorithm, 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 identification results show that the feedback GMDH-type neural network algorithm is useful for the medical image diagnosis of lung cancer since the optimum neural network architecture is automatically organized so as to fit the complexity of the medical images.