Application of artificial neural networks to chromosome classification

This work presents an approach to the automatic classification of metaphase chromosomes using a multilayer perceptron neural network. Representation of the banding patterns by intuitively defined features is avoided. The inputs to the network are the chromosome size and centromeric index and a coarsely quantized representation of the chromosome banding profile. We demonstrate that following a fairly mechanical training procedure, the classification performance of the network compares favourably with a well-developed parametric classifier. The sensitivity of the network performance to variation in network parameters is investigated, and we show that a gain in efficiency is obtainable by an appropriate decomposition of the network. We discuss the flexibility of the classifier developed, its potential for enhancement, and how it may be adapted to suit the needs of current trends in karyotyping.