Medical image diagnosis of liver cancer using deep RBF GMDH-type neural network

Abstract

In this study, a deep Radial Basis Function (RBF) Group Method of Data Handling (GMDH)-type neural network which has the deep neural network architecture, is applied to the medical image diagnosis of liver cancer. Deep RBF GMDH-type neural network has abilities of self-selecting the number of hidden layers, the number of neurons in hidden layers and useful input variables. This algorithm is applied to medical image recognition of liver cancer and it is shown that this algorithm is useful for medical image diagnosis of liver cancer and is very easy to apply practical complex problem because deep neural network architecture with many hidden layers, is automatically organized so as to minimize the prediction error criterion defined as Akaike’s Information Criterion (AIC) or Prediction Sum of Squares (PSS).