Logistic GMDH-type neural network using principal component-regression analysis and its application to medical image diagnosis of lung cancer

Abstract

The logistic group method of data handling (GMDH)-type neural network is proposed and applied to the medical image diagnosis of lung cancer. In this logistic GMDH-type neural network algorithm, the principal component-regression analysis is used to estimate the parameters of the neural network, and the multi-colinearity, which is generated in the conventional GMDH algorithm, is protected. Therefore, accurate and stable predicted values are obtained in the logistic GMDH-type neural network. Furthermore, the polynomial and logistic neurons are used, and the neural network architectures are automatically organized so as to minimize the prediction error criterion defined as Akaike's information criterion or prediction sum of squares. The identification results show that the logistic 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.