Medical image diagnosis of lung cancer by multi-layered GMDH-type neural network self-selecting functions

被引：6

作者：

Kondo T. ^{[1
]}

Ueno J. ^{[1
]}

Takao S. ^{[1
]}

机构：

[1] Graduate School of Health Sciences, The University of Tokushima, Tokushima, 770-8509

来源：

Kondo, T. (kondo@medsci.tokushima-u.ac.jp) | 1600年 / Springer Japan卷 / 18期

基金：

日本学术振兴会;

关键词：

GMDH; Medical image diagnosis; Neural networks;

D O I：

10.1007/s10015-013-0094-0

中图分类号：

学科分类号：

摘要：

In this study, a revised Group Method of Data Handling (GMDH)-type neural network self-selecting functions 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 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 of lung cancers, and it is shown that this algorithm is useful for the CAD, and is very easy to apply to practical complex problems because optimum neural network architecture is automatically organized. © 2013 ISAROB.

引用

页码：20 / 26

页数：6