in situ
hybridisation (FISH) signals. Highly-accurate classification of valid signals and artifacts of several cytogenetic probes
(colours) is required for detecting abnormalities in FISH images. More than 3100 FISH signals are classified by each of the
techniques into colour and as real or artifact with accuracies of around 98% and 88%, respectively. The results of the comparison
also show a trade-off between simplicity represented by the naive Bayesian classifier, and high classification performance
represented by the other techniques.
Keywords:Bayesian neural network; Fluorescence in situ hybridisation (FISH); Multilayer perceptron; Naive Bayesian classifier;
Signal classification; Support vector machine