Neural network classification of pediatric posterior fossa tumors using clinical and imaging data

Shaad Bidiwala, Thomas Pittman

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

A neural network was developed that utilizes both clinical and imaging (CT and MRI) data to predict posterior fossa tumor (PFT) type. Data from 33 children with PFTs were used to develop and test the system. When all desired information was available, the network was able to correctly classify 85.7% of the tumors. In cases with incomplete data, it was able to correctly classify 72.7% of the tumors. In both instances, the diagnoses made by the network were more likely to be correct than those made by the neuroradiologists.

Original languageEnglish
Pages (from-to)8-15
Number of pages8
JournalPediatric Neurosurgery
Volume40
Issue number1
DOIs
StatePublished - 2004

Keywords

  • Children
  • Expert system
  • Neural network
  • Posterior fossa tumors

ASJC Scopus subject areas

  • Pediatrics, Perinatology, and Child Health
  • Surgery
  • Clinical Neurology

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