SVM optimization for hyperspectral colon tissue cell classification

Kashif Rajpoot; Nasir Rajpoot

doi:10.1007/978-3-540-30136-3_101

SVM optimization for hyperspectral colon tissue cell classification

Kashif Rajpoot
, Nasir Rajpoot

Computer Science

Research output: Contribution to journal › Conference article › peer-review

Abstract

The classification of normal and malginant colon tissue cells is crucial to the diagnosis of colon cancer in humans. Given the right set of feature vectors, Support Vector Machines (SVMs) have been shown to perform reasonably well for the classification. In this paper, we address the following question: how does the choice of a kernel function and its parameters affect the SVM classification performance in such a system? We show that the Gaussian kernel function combined with an optimal choice of parameters can produce high classification accuracy.

Original language	English
Pages (from-to)	829-837
Number of pages	9
Journal	Lecture Notes in Computer Science
Volume	3217
Issue number	1 PART 2
DOIs	https://doi.org/10.1007/978-3-540-30136-3_101
Publication status	Published - 2004
Event	Medical Image Computing and Computer-Assisted Intervention, MICCAI 2004 - 7th International Conference, Proceedings - Saint-Malo, France Duration: 26 Sept 2004 → 29 Sept 2004

UN SDGs

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

SDG 3 Good Health and Well-being

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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10.1007/978-3-540-30136-3_101

Cite this

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abstract = "The classification of normal and malginant colon tissue cells is crucial to the diagnosis of colon cancer in humans. Given the right set of feature vectors, Support Vector Machines (SVMs) have been shown to perform reasonably well for the classification. In this paper, we address the following question: how does the choice of a kernel function and its parameters affect the SVM classification performance in such a system? We show that the Gaussian kernel function combined with an optimal choice of parameters can produce high classification accuracy.",

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AU - Rajpoot, Nasir

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AB - The classification of normal and malginant colon tissue cells is crucial to the diagnosis of colon cancer in humans. Given the right set of feature vectors, Support Vector Machines (SVMs) have been shown to perform reasonably well for the classification. In this paper, we address the following question: how does the choice of a kernel function and its parameters affect the SVM classification performance in such a system? We show that the Gaussian kernel function combined with an optimal choice of parameters can produce high classification accuracy.

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U2 - 10.1007/978-3-540-30136-3_101

DO - 10.1007/978-3-540-30136-3_101

M3 - Conference article

AN - SCOPUS:20344371920

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JO - Lecture Notes in Computer Science

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Y2 - 26 September 2004 through 29 September 2004

ER -

SVM optimization for hyperspectral colon tissue cell classification

Abstract

UN SDGs

ASJC Scopus subject areas

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