By Topic

Feedback GMDH-type Neural Network Self-Selecting Various Functions and Its Application to Medical Image Diagnosis of Lung Cancer

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
$33 $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

3 Author(s)
Tadashi Kondo ; Grad. Sch. of Health Sci., Univ. of Tokushima, Tokushima, Japan ; Junji Ueno ; Shoichiro Takao

The feedback Group Method of Data Handling (GMDH) -type neural network algorithm is applied to the medical image diagnosis of lung cancer. In this feedback GMDH-type neural network algorithm, the structural parameters such as the number of feedback loops, the number of neurons in the hidden layers and the relevant input variables are automatically selected so as to minimize the prediction error criterion defined as Akaike's Information Criterion (AIC) or Prediction Sum of Squares (PSS). The identification results show that the feedback GMDH-type neural network algorithm is useful for the medical image diagnosis of lung cancer since the optimum neural network architecture is automatically organized so as to fit the complexity of the medical images.

Published in:

Software Engineering, Artificial Intelligence, Networking and Parallel & Distributed Computing (SNPD), 2012 13th ACIS International Conference on

Date of Conference:

8-10 Aug. 2012