Discriminant method for severity of glandular tumor by support vector machine

Ayako Suzuki; Toshiyuki Tanaka

doi:10.1109/SICE.2008.4655197

Discriminant method for severity of glandular tumor by support vector machine

Ayako Suzuki, Toshiyuki Tanaka

Department of Applied Physics and Physico-Informatics

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Proceedings of SICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology
Pages	3101-3104
Number of pages	4
DOIs	https://doi.org/10.1109/SICE.2008.4655197
Publication status	Published - 2008 Dec 1
Event	SICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology - Tokyo, Japan Duration: 2008 Aug 20 → 2008 Aug 22

Publication series

Name	Proceedings of the SICE Annual Conference

Other

Other	SICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology
Country	Japan
City	Tokyo
Period	08/8/20 → 08/8/22

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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

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

Proceedings of SICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology. 2008. p. 3101-3104 4655197 (Proceedings of the SICE Annual Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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., 4655197, Proceedings of the SICE Annual Conference, 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

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Access to Document

10.1109/SICE.2008.4655197