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 the SICE Annual Conference
Pages3101-3104
Number of pages4
DOIs
Publication statusPublished - 2008
EventSICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology - Tokyo, Japan
Duration: 2008 Aug 202008 Aug 22

Other

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

Fingerprint

Discriminant analysis
Support vector machines
Tumors

Keywords

  • Discriminant analysis
  • Support vector machine
  • Texture analysis

ASJC Scopus subject areas

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

Cite this

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

Discriminant method for severity of glandular tumor by support vector machine. / Suzuki, Ayako; Tanaka, Toshiyuki.

Proceedings of the SICE Annual Conference. 2008. p. 3101-3104 4655197.

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

Suzuki, A & Tanaka, T 2008, Discriminant method for severity of glandular tumor by support vector machine. in Proceedings of the SICE Annual Conference., 4655197, pp. 3101-3104, SICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology, Tokyo, Japan, 08/8/20. https://doi.org/10.1109/SICE.2008.4655197
Suzuki A, Tanaka T. Discriminant method for severity of glandular tumor by support vector machine. In Proceedings of the SICE Annual Conference. 2008. p. 3101-3104. 4655197 https://doi.org/10.1109/SICE.2008.4655197
Suzuki, Ayako ; Tanaka, Toshiyuki. / Discriminant method for severity of glandular tumor by support vector machine. Proceedings of the SICE Annual Conference. 2008. pp. 3101-3104
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