The Implementation of Pattern Classifier for Karyotype Classification.
- Author:
Yong Hoon CHANG
1
;
Kwon Soon LEE
;
Gye Rok JUN
Author Information
1. Dong-Ju Women's Junior College, Korea.
- Publication Type:Original Article
- Keywords:
Chromosome;
Karyotype;
Multi-layer Neural Network;
Morphological feature;
Multi-step classifier
- MeSH:
Chromosomes, Human;
Classification*;
Humans;
Karyotype*
- From:Journal of Korean Society of Medical Informatics
1997;3(2):207-214
- CountryRepublic of Korea
- Language:Korean
-
Abstract:
The human chromosome analysis is widely used to diagnose genetic disease and various congenital anomalies. Many researches on automated chromosome karyotype analysis has been carried out, some of which produced commercial systems. However, there still remains much room for improving the accuracy of chromosome classification. In this paper, We propose an optimal pattern classifier by neural network to improve the accuracy of chromosome classification. The proposed pattern classifier was built up of multi-step multi-layer neural network(MMANN). We reconstructed chromosome image to improve the chromosome classification accuracy and extracted three morphological features parameters such as centromeric index(C.1.), relative length ratio(R.L.), and relative area ratio(R.A.). This Parameters employed as input in neural network by preprocessing twenty human chromosome images. The experiment results show that the chromosome classification error is reduced much more than that of the other classification methods.
