By Topic

Classification of heart diseases in ultrasonic images using neural networks trained by genetic algorithms

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

1 Author(s)
Du-Yih Tsai ; Dept. of Electr. Eng., Gifu Nat. Coll. of Technol., Japan

Recent studies show the effectiveness of neural-network-based computer-aided-diagnosis schemes for automated detection of various diseases, such as malignant breast mass and lung nodules. In this paper we describe a method for automated classification of ultrasonic heart (echocardiographic) images. The feature of the method is to employ an artificial neural network (NN) trained by genetic algorithms (GA's) instead of backpropagation. With the GA the optimal weighting coefficients of the NN are determined. Also the method shows a faster convergence for obtaining the optimal solution in NN training. Experiments on different data sets show the superiority of the GA-based method over backpropagation for classification

Published in:

Signal Processing Proceedings, 1998. ICSP '98. 1998 Fourth International Conference on  (Volume:2 )

Date of Conference: