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

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

doi:10.1007/bf02493239

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 journal › Article › peer-review

3 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 language	English
Pages (from-to)	137-142
Number of pages	6
Journal	Oral Radiology
Volume	19
Issue number	2
DOIs	https://doi.org/10.1007/bf02493239
Publication status	Published - 2003
Externally published	Yes

Keywords

Computer-aided diagnosis
Lymph node metastasis
Neural networks

ASJC Scopus subject areas

Dentistry (miscellaneous)
Radiology Nuclear Medicine and imaging

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/bf02493239

Cite this

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title = "Application of Neural Networks to the Prediction of Lymph Node Metastasis in Oral Cancer",

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.",

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journal = "Oral Radiology",

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TY - JOUR

T1 - Application of Neural Networks to the Prediction of Lymph Node Metastasis in Oral Cancer

AU - Kawazu, Toshiyuki

AU - Araki, Kazuyuki

AU - Yoshiura, Kazunori

AU - Nakayama, Eiji

AU - Kanda, Shigenobu

PY - 2003

Y1 - 2003

N2 - 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.

AB - 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.

KW - Computer-aided diagnosis

KW - Lymph node metastasis

KW - Neural networks

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DO - 10.1007/bf02493239

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JO - Oral Radiology

JF - Oral Radiology

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

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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