A study for the hierarchical artificial neural network model for Giemsa-stained human chromosome classification

  title={A study for the hierarchical artificial neural network model for Giemsa-stained human chromosome classification},
  author={J. M. Cho and S. Y. Ryu and Sungtae Woo},
  journal={The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
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… CONTINUE READING

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