Medical image diagnosis of lung cancer by revised GMDH-type neural network self-selecting optimum neuron architectures
Tadashi Kondo, Junji Ueno, Shoichiro Takao · 2011
In this study, a revised Group Method of Data Handling (GMDH)-type neural network self-selecting optimum neuron architectures is applied to the computer aided image diagnosis (CAD) of lung cancer. The GMDH-type neural network algorithm has an ability of self-selecting optimum neural network architecture from three neural network architectures such as sigmoid function neural network, radial basis function (RBF) neural network and polynomial neural network. The GMDH-type neural network also has abilities of self-selecting the number of layers, the number of neurons in hidden layers and useful input variables. This algorithm is applied to CAD and it is shown that this algorithm is useful for CAD of lung cancer and is very easy to apply practical complex problem because optimum neural network architecture is automatically organized.