A NEURAL-NETWORK APPROACH TO AUTOMATIC CHROMOSOME CLASSIFICATION

Classification of banded metaphase chromosomes is an important step in automated clinical chromosome analysis. We have conducted a preliminary investigation of the application of artificial neural networks to this process, making use of a natural representation of the banding pattern. Two different network architectures have been compared: the Kohonen self-organizing feature map and the multi-layer perception (MLP). For each of these a search of their respective parameter spaces over a limited range has resulted in configurations of modest dimension which achieve creditable classification rates. The MLP in particular shows promise of being a useful classifier. When size and shape features are supplied as inputs to the MLP in addition to a low-resolution banding profile, misclassification rates are obtained which are comparable with those of a well developed statistical classifier.