Medical image recognition of cancer of the liver by GMDH-type neural network
Tadashi Kondo, Masahiro Marshall Nakagawa, Shoichiro Takao, Junji Ueno · Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications · 2010
In this study, the multi-layered Group Method of Data Handling (GMDH)-type neural network self-selecting optimum neural network architecture is applied to the computer aided image diagnosis (CAD) of the cancer of the liver. 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 have 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 the cancer of the liver and is very easy to apply practical complex problem because optimum neural network architecture is automatically organized.