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 chromo-somes. 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 these 7 major groups into 24 sub-groups using each group classifier. The classification error decreased by using two steps of classification and the classify-cation 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.
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Cho, J. (2007). A Hierarchical Artificial Neural Network Model for Giemsa-Stained Human Chromosome Classification. In: Ibrahim, F., Osman, N.A.A., Usman, J., Kadri, N.A. (eds) 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006. IFMBE Proceedings, vol 15. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68017-8_5
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DOI: https://doi.org/10.1007/978-3-540-68017-8_5
Publisher Name: Springer, Berlin, Heidelberg
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