Document Detail

Human chromosome classification using multilayer perceptron neural network.
MedLine Citation:
PMID:  8589868     Owner:  NLM     Status:  MEDLINE    
A multilayer perceptron (MLP) neural network (NN) has been studied for human chromosome classification. Only 10-20 examples were required for the MLP NN to reach its ultimate performance classifying chromosomes of 5 types. The empirical dependence of the entropic error on the number of examples was found to be highly comparable to the 1/t function. The principal component analysis (PCA) was used, both for network initialization and for feature reduction purposes. The PCA demonstrated the importance of retaining most of the image information whenever small training sets are used. The MLP NN classifier outperformed the Bayes piecewise classifier for all the cases tested. The MLP classifier was found to be almost unsusceptible to the ratio of the number of training vectors to the number of features, whereas the piecewise classifier was highly dependent on this ratio.
B Lerner; H Guterman; I Dinstein; Y Romem
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  International journal of neural systems     Volume:  6     ISSN:  0129-0657     ISO Abbreviation:  Int J Neural Syst     Publication Date:  1995 Sep 
Date Detail:
Created Date:  1996-03-27     Completed Date:  1996-03-27     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  9100527     Medline TA:  Int J Neural Syst     Country:  SINGAPORE    
Other Details:
Languages:  eng     Pagination:  359-70     Citation Subset:  IM    
Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
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MeSH Terms
Amniotic Fluid / cytology
Artificial Intelligence
Bayes Theorem
Chromosomes, Human / classification*
Karyotyping / methods*
Neural Networks (Computer)*
Probability Learning

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