A comparison of state-of-the-art classification techniques with application to cytogenetics
Several state-of-the-art techniques - a neural network, Bayesian neural network, support vector machine and naive Bayesian classifier - are experimentally evaluated in discriminating fluorescence 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.
bayesian neural network support vector machine naive bayesian classifier fluorescence in situ hybridisation (fish) multilayer perceptron signal classification