By Topic

Classification of Liver Disease from CT Images Using Sigmoid Radial Basis Function Neural Network

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)
Chien-Cheng Lee ; Dept. of Commun. Eng., Yuan Ze Univ., Chungli, Taiwan ; Cheng-Yuan Shih

The aim of this paper is to discriminate liver diseases from CT images automatically using a sigmoid radial basis function neural network with growing and pruning algorithm (SRBFNN-GAP). We develop a novel SRBFNN-GAP to discriminate cyst, hepatoma, cavernous hemangioma, and normal tissue using gray level and Gabor texture features. The proposed SRBFNN adopts sigmoid function as its kernel because the sigmoid function provides a more flexible shape than Gaussian. Furthermore, the GAP algorithm is used to adjust the network size dynamically according to the neuronpsilas significance. In the experiment, the SRBFNN-GAP classifies the features into four classes, and the receiver operating characteristic (ROC) curve is used to evaluate the diagnosis performance.

Published in:

Computer Science and Information Engineering, 2009 WRI World Congress on  (Volume:5 )

Date of Conference:

March 31 2009-April 2 2009