By Topic

Medical image diagnosis of lung cancer by revised GMDH-type neural network using heuristic self-organization

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Kondo, T. ; Grad. Sch. of Health Sci., Univ. of Tokushima, Tokushima, Japan ; Ueno, J.

In this study, a revised Group Method of Data Handling (GMDH)-type neural network using heuristic self-organization 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.

Published in:

SICE Annual Conference (SICE), 2011 Proceedings of

Date of Conference:

13-18 Sept. 2011