Application of Neural Networks to the Prediction of Lymph Node Metastasis in Oral Cancer

Toshiyuki Kawazu, Kazuyuki Araki, Kazunori Yoshiura, Eiji Nakayama, Shigenobu Kanda

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Neural networks are a new type of computing algorithm. They are especially useful in pattern recognition. In this study we applied neural networks to the prediction of lymph node metastasis of patients with oral cancer. A data set of 1,116 lymph nodes verified histopathologically was used to train and evaluate the neural networks. Various three-layer feed-forward networks with a back-propagation algorithm were employed in this study. Performance of the neural networks was compared with that of radiologists and discriminant analysis (Quantification theory type II). Neural networks had a sensitivity of 80.6% and a specificity of 94.6%. Diagnostic accuracy of the neural networks was 93.6%, which was comparable to those of discriminant analysis and clinical radiologists.

Original languageEnglish
Pages (from-to)137-142
Number of pages6
JournalOral Radiology
Volume19
Issue number2
DOIs
Publication statusPublished - Jan 1 2003
Externally publishedYes

Keywords

  • Computer-aided diagnosis
  • Lymph node metastasis
  • Neural networks

ASJC Scopus subject areas

  • Dentistry (miscellaneous)
  • Radiology Nuclear Medicine and imaging

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