A new support vector machine method for medical image classification

Dat Tran; Wanli Ma; Dharmendra Sharma

doi:10.1109/EUVIP.2010.5699139

A new support vector machine method for medical image classification

Dat Tran, Wanli Ma, Dharmendra Sharma

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

6 Citations (Scopus)

48 Downloads (Pure)

Abstract

One of the important problems in medical imaging is two-class classification, for example determination of benign from malignant cases in breast cancer treatment. In this paper we present a new support vector machine method for two-class medical image classification. The key idea of this method is to construct an optimal hypersphere such that both the interior margin between the surface of this sphere and the normal data, and the exterior margin between this surface and the abnormal data are as large as possible. The proposed method is easily implemented and can reduce both false positive and false negative error rates to obtain very good classification results. Experiments were performed on three medical image data sets to evaluate the proposed method.

Original language	English
Title of host publication	2010 2nd European Workshop on Visual Information Processing (EUVIP)
Place of Publication	USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	165-170
Number of pages	6
ISBN (Print)	9781424472871
DOIs	https://doi.org/10.1109/EUVIP.2010.5699139
Publication status	Published - 2010
Event	2nd European Workshop on Visual Information Processing: EUVIP 2010 - Paris, France Duration: 5 Jul 2010 → 7 Jul 2010

Conference

Conference	2nd European Workshop on Visual Information Processing: EUVIP 2010
Country/Territory	France
City	Paris
Period	5/07/10 → 7/07/10

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10.1109/EUVIP.2010.5699139

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title = "A new support vector machine method for medical image classification",

abstract = "One of the important problems in medical imaging is two-class classification, for example determination of benign from malignant cases in breast cancer treatment. In this paper we present a new support vector machine method for two-class medical image classification. The key idea of this method is to construct an optimal hypersphere such that both the interior margin between the surface of this sphere and the normal data, and the exterior margin between this surface and the abnormal data are as large as possible. The proposed method is easily implemented and can reduce both false positive and false negative error rates to obtain very good classification results. Experiments were performed on three medical image data sets to evaluate the proposed method.",

author = "Dat Tran and Wanli Ma and Dharmendra Sharma",

year = "2010",

doi = "10.1109/EUVIP.2010.5699139",

language = "English",

isbn = "9781424472871",

pages = "165--170",

booktitle = "2010 2nd European Workshop on Visual Information Processing (EUVIP)",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

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note = "2nd European Workshop on Visual Information Processing: EUVIP 2010 ; Conference date: 05-07-2010 Through 07-07-2010",

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Tran, D , Ma, W & Sharma, D 2010, A new support vector machine method for medical image classification. in 2010 2nd European Workshop on Visual Information Processing (EUVIP). IEEE, Institute of Electrical and Electronics Engineers, USA, pp. 165-170, 2nd European Workshop on Visual Information Processing: EUVIP 2010, Paris, France, 5/07/10. https://doi.org/10.1109/EUVIP.2010.5699139

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AU - Tran, Dat

AU - Ma, Wanli

AU - Sharma, Dharmendra

PY - 2010

Y1 - 2010

N2 - One of the important problems in medical imaging is two-class classification, for example determination of benign from malignant cases in breast cancer treatment. In this paper we present a new support vector machine method for two-class medical image classification. The key idea of this method is to construct an optimal hypersphere such that both the interior margin between the surface of this sphere and the normal data, and the exterior margin between this surface and the abnormal data are as large as possible. The proposed method is easily implemented and can reduce both false positive and false negative error rates to obtain very good classification results. Experiments were performed on three medical image data sets to evaluate the proposed method.

AB - One of the important problems in medical imaging is two-class classification, for example determination of benign from malignant cases in breast cancer treatment. In this paper we present a new support vector machine method for two-class medical image classification. The key idea of this method is to construct an optimal hypersphere such that both the interior margin between the surface of this sphere and the normal data, and the exterior margin between this surface and the abnormal data are as large as possible. The proposed method is easily implemented and can reduce both false positive and false negative error rates to obtain very good classification results. Experiments were performed on three medical image data sets to evaluate the proposed method.

U2 - 10.1109/EUVIP.2010.5699139

DO - 10.1109/EUVIP.2010.5699139

M3 - Conference contribution

SN - 9781424472871

SP - 165

EP - 170

BT - 2010 2nd European Workshop on Visual Information Processing (EUVIP)

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - USA

T2 - 2nd European Workshop on Visual Information Processing: EUVIP 2010

Y2 - 5 July 2010 through 7 July 2010

ER -

A new support vector machine method for medical image classification

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