Hybrid feedback GMDH-type neural network using principal component-regression analysis and its application to medical image recognition of heart regions

Abstract

Hybrid feedback Group Method of Data Handling (GMDH)-type neural network using principal component-regression analysis is applied to the medical image recognition of the heart regions. In the GMDH-type neural network, the multi-layered deep neural networks are automatically organized so as to fit the complexity of the nonlinear system and, in general, the architectures of the GMDH-type neural network have many hidden layers and become complex for the nonlinear systems. In the multi-layered deep GMDH-type neural network with many hidden layers, the multi-colinearity occurs and the perdition accuracy become worse and the prediction values become unstable. In the GMDH-type neural network used in this study, the principal component-regression analysis is used as the learning algorithm of the neural network and the multi-colinearity do not occur and accurate and stable GMDH-type neural network architectures are automatically organized so as to fit the complexity of the nonlinear system. Furthermore, in this algorithm, three types of neural networks, such as sigmoid function neural network, radial basis function (RBF) neural network and polynomial neural network, can be generated using three types of neuron architectures, and the neural network architecture which fits the complexity of medical images, is selected from these three neural network architectures. This GMDH-type neural network is applied to the medical image recognition of the heart regions and it is shown that this GMDH-type neural network is useful for the medical image recognition of the heart regions.