By Topic

The number of processing elements in hidden-layer of back-propagation neural network for karyotyping [genetic diagnosis]

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)
Jongman Cho ; Dept. of Med. Eng., Inje Univ., Kimhae, South Korea ; SeungHong Hong

Back-propagation neural networks with various number of processing elements (PEs) in hidden-layer were examined to karyotype of Giemsa-stained human chromosomes. Two learning sets for the experiments were prepared from randomly selected 460 chromosomes. Learning set A consisted of 27 vectors, which included a relative length, a centromeric index, and 25 density vectors extracted from normalized density profile. Learning set B was the same as the learning set A but it had 50 density vectors. For the two learning sets the classification errors in output layer were examined with various number of PEs in hidden layer. Results of the experiment showed that the minimum classification error was obtained in a model trained with 27 input vectors and 48 PEs in its hidden layer

Published in:

Engineering in Medicine and Biology Society, 1995., IEEE 17th Annual Conference  (Volume:1 )

Date of Conference:

20-25 Sep 1995