A new support vector machine method for medical image classification

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    5 Citations (Scopus)

    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 languageEnglish
    Title of host publication2010 2nd European Workshop on Visual Information Processing (EUVIP)
    Place of PublicationUSA
    PublisherIEEE
    Pages165-170
    Number of pages6
    ISBN (Print)9781424472871
    DOIs
    Publication statusPublished - 2010
    Event2nd European Workshop on Visual Information Processing: EUVIP 2010 - Paris, France
    Duration: 5 Jul 20107 Jul 2010

    Conference

    Conference2nd European Workshop on Visual Information Processing: EUVIP 2010
    CountryFrance
    CityParis
    Period5/07/107/07/10

    Fingerprint

    Image classification
    Support vector machines
    Oncology
    Medical imaging
    Experiments

    Cite this

    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) (pp. 165-170). USA: IEEE. https://doi.org/10.1109/EUVIP.2010.5699139
    Tran, Dat ; Ma, Wanli ; Sharma, Dharmendra. / A new support vector machine method for medical image classification. 2010 2nd European Workshop on Visual Information Processing (EUVIP). USA : IEEE, 2010. pp. 165-170
    @inproceedings{4ef33e04016643579fbb20632e86410f,
    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)",
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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, 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

    A new support vector machine method for medical image classification. / Tran, Dat; Ma, Wanli; Sharma, Dharmendra.

    2010 2nd European Workshop on Visual Information Processing (EUVIP). USA : IEEE, 2010. p. 165-170.

    Research output: A Conference proceeding or a Chapter in BookConference contribution

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    Tran D, Ma W, Sharma D. A new support vector machine method for medical image classification. In 2010 2nd European Workshop on Visual Information Processing (EUVIP). USA: IEEE. 2010. p. 165-170 https://doi.org/10.1109/EUVIP.2010.5699139