Belief networks for cytogenetic image categorization

Boaz Lerner, Roy Malka

Research output: Contribution to conferencePaperpeer-review

Abstract

The structure and parameters of a belief network are learned in order to categorize cytogenetic images enabling the detection of genetic syndromes. We compare a structure learned from the data to another obtained utilizing expert knowledge and to the naive Bayesian classifier. We also study feature quantization needed for parameter learning in comparison to density estimation. Both networks achieve comparable accuracy for the cytogenetic database with a slight advantage to that based on expert knowledge.

Original languageEnglish
Pages297-300
Number of pages4
StatePublished - 1 Dec 2004
Event2004 23rd IEEE Convention of Electrical and Electronics Engineers in Israel, Proceedings - Tel-Aviv, Israel
Duration: 6 Sep 20047 Sep 2004

Conference

Conference2004 23rd IEEE Convention of Electrical and Electronics Engineers in Israel, Proceedings
Country/TerritoryIsrael
CityTel-Aviv
Period6/09/047/09/04

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials

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