Chromosome classification using backpropagation neural networks.

Abstract

The feasibility of an artificial neural network as a chromosome classifier was examined in this study, using the relative length, the centromeric index, and the density distribution of G-banded chromosome as feature vectors. The two-layer neural network trained with the error backpropagation training algorithm showed good potential in classification of Giemsa-banded human chromosomes. The minimum classification error was obtained with the configuration that had 27 input nodes and 24 PEs in the hidden layer. However, this study also showed some problems. Only two experiments, which had 25 and 50 density distribution samples, respectively, were carried out, due to the long computation time of the backpropagation neural network. Also, the centromere finding algorithm used in this study could not apply to telocentric chromosomes (group D and group G) because of their very small short arms; their centromere locations were determined manually. The algorithm must be modified so that it can be applied to all types of chromosomes to reduce the preprocessing time. Better training algorithms to reduce training time are needed. The error backpropagation training algorithm requires very long training times. Next, finding the optimal number of input nodes that gives the minimum classification error requires a trial and error experiment. Finally, other chromosome features that reduce the classification error need to be examined.