Application of Ensemble Machines of Neural Networks to Chromosome Classification

Abstract

This work presents approaches to the automatic classification of metaphase chromosomes using several perceptron neural network techniques on neural networks function as committee machines. To represent the banding patterns, only chromosome gray level profiles are exploited. The other inputs to the ensemble machines of the network are the chromosome size and centromeric index. It is shown that, without much effort, the classification performances of the four networks are found to be similar to the ones of a well-developed parametric classifier. Four parallel networks trained for the four different aspects of the data set, the gray level profile vector, Fourier coefficients of gray level profiles, 3D data of chromosome length – centromeric index – total gray levels, and 4D data obtained by the addition of average gray levels. Then the classification results of differently trained neural networks (i.e., experts), are combined by the use of a genuine ensemble-averaging to produce an overall output by the combiner. We discuss the flexibility of the classifier developed, its potential for development, and how it may be improved to suit the current needs in karyotyping.