Medical image recognition of the brain by revised GMDH-type neural network algorithm with a feedback loop
Tadashi Kondo, Junji Ueno · International journal of innovative computing, information & control · 2006
In this paper, a revised Group Method of Data Handling (GMDH)-type neural network algorithm with a feedback loop identifying sigmoid function neural network is applied to the medical image recognition of the brain. This revised GMDH-type neural network algorithm automatically selects the structural parameters such as the number of neurons in each layer, the number of feedback loops and the useful input variables using Akaike’s Information Criterion (AIC) or Prediction Sum of Squares (PSS) criterion. It is shown that this revised GMDH-type neural network is a very useful method for the medical image recognition because the neural network architecture is automatically organized so as to minimize AIC or PSS criterion.