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A Comparison of State-of-the-Art Classification Techniques with Application to Cytogenetics
  • Published: April 2001
  • Volume 10, pages 39–47, (2001)
  • Cite this article
Download PDF Neural Computing & Applications Aims and scope Submit manuscript A Comparison of State-of-the-Art Classification Techniques with Application to Cytogenetics Download PDF

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.

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  1. Computer Laboratory, University of Cambridge, Cambridge, UK, , , , , , GB

    Boaz Lerner & Neil D. Lawrence

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Lerner, B., Lawrence, N. A Comparison of State-of-the-Art Classification Techniques with Application to Cytogenetics . Neural Computing & Applications 10, 39–47 (2001). https://doi.org/10.1007/s005210170016

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  • Issue date: April 2001

  • DOI: https://doi.org/10.1007/s005210170016

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