A comparison of state-of-the-art classification techniques with application to cytogenetics

Boaz Lerner, Neil D. Lawrence

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


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.

Original languageEnglish
Pages (from-to)39-47
Number of pages9
JournalNeural Computing and Applications
Issue number1
StatePublished - 1 Dec 2001
Externally publishedYes


  • Bayesian neural network
  • Fluorescence in situ hybridisation (FISH)
  • Multilayer perceptron
  • Naive Bayesian classifier
  • Signal classification
  • Support vector machine

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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