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

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.