신경회로망을 이용한 염색체 영상의 최적 패턴 분류기 구현

Implementation on Optimal Pattern Classifier of Chromosome Image using Neural Network

Chromosomes, as the genetic vehicles, provide the basic material for a large proportion of genetic investigations. 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 two-step multi-layer neural network(TMANN). We are employed three morphological feature parameters ; centromeric index(C.I.), relative length ratio(R.L.), and relative area ratio(R.A.), as input in neural network by preprocessing twenty human chromosome images. The results of our experiments show that our TMANN classifier is much more useful in neural network learning and successful in chromosome classification than the other classification methods.