Discriminant method for severity of glandular tumor by support vector machine

Ayako Suzuki, Toshiyuki Tanaka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this study, glandular tumor images are classified automatically by the support vector machine (SVM) in order to make up for a fault of discriminant analysis, Mahalanobis' generalized distance which was used in recent studies. The fault of Mahalanobis' generalized distance is the problem, that is to say, the Curse of Dimensionality. To avoid this problem, we used the support vector machine (SVM) as the discriminant analysis, used the prostate images as glandular tumor images ,and examined the effectiveness of this system.

Original languageEnglish
Title of host publicationProceedings of SICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology
Pages3101-3104
Number of pages4
DOIs
Publication statusPublished - 2008 Dec 1
EventSICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology - Tokyo, Japan
Duration: 2008 Aug 202008 Aug 22

Publication series

NameProceedings of the SICE Annual Conference

Other

OtherSICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology
CountryJapan
CityTokyo
Period08/8/2008/8/22

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Keywords

  • Discriminant analysis
  • Support vector machine
  • Texture analysis

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Suzuki, A., & Tanaka, T. (2008). Discriminant method for severity of glandular tumor by support vector machine. In Proceedings of SICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology (pp. 3101-3104). [4655197] (Proceedings of the SICE Annual Conference). https://doi.org/10.1109/SICE.2008.4655197