DocumentCode :
2745682
Title :
A study for the hierarchical artificial neural network model for Giemsa-stained human chromosome classification
Author :
Cho, J. ; Ryu, S.Y. ; Woo, S.H.
Author_Institution :
Dept. of Biomedical Eng., Inje Univ., South Korea
Volume :
2
fYear :
2004
fDate :
1-5 Sept. 2004
Firstpage :
4588
Lastpage :
4591
Abstract :
A hierarchical multi-layer neural network with an error back-propagation training algorithm has been adopted for the automatic classification of Giemsa-stained human chromosomes. The first step classifies chromosomes data into 7 major groups based on their morphological features such as relative length, relative area, centromeric index, and 80 density profiles. The second step classifies each 7 major groups into 24 subgroups using each group classifier. The classification error decreased by using two steps of classification and the classification error was 5.9%. The result of this study shows that a hierarchical multi-layer neural network can be accepted as an automatic human chromosome classifier.
Keywords :
biology computing; cellular biophysics; neural nets; Giemsa-stained human chromosome classification; centromeric index; error back-propagation training algorithm; hierarchical artificial neural network model; Artificial neural networks; Biological cells; Cancer; Cells (biology); Genetics; Humans; Image analysis; Multi-layer neural network; Spatial databases; Visual databases; Chromosome; hierarchical multi-layer neural network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-8439-3
Type :
conf
DOI :
10.1109/IEMBS.2004.1404272
Filename :
1404272
Link To Document :
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