Mixed kernel function SVM for pulmonary nodule recognition

Yang Li, Dunwei Wen, Ke Wang, A'lin Hou

    Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

    9 Citations (Scopus)

    Abstract

    Automatic pulmonary nodule detection in computed tomography (CT) images has been a challenging problem in computer aided diagnosis (CAD). Most recent recognition methods based on support vector machines (SVMs) have shown difficulty in achieving balanced sensitivity and accuracy. To improve overall performance of SVM based pulmonary nodule detection, a mixed kernel SVM method is proposed for recognizing pulmonary nodules in CT images by combining both Gaussian and polynomial kernel functions. The proposed mixed kernel SVM, together with a grid search for parameters optimization, can be tuned to seek a balance between sensitivity and accuracy so as to meet the CADs need, and eventually to improve learning and generalization ability of the SVM at the same time. In our experiments, thirteen features were extracted from the candidate regions of interest (ROIs) preprocessed from a set of real CT samples, and the mixed kernel SVM was trained to recognize the nodules in the ROIs. The results show that the proposed method takes into account both the sensitivity and accuracy compared to single kernel SVMs. The sensitivity and accuracy of the proposed method achieve 92.59% and 92% respectively.

    Original languageEnglish
    Title of host publicationImage Analysis and Processing, ICIAP 2013 - 17th International Conference, Proceedings
    Pages449-458
    Number of pages10
    EditionPART 2
    DOIs
    Publication statusPublished - 2013
    Event17th International Conference on Image Analysis and Processing, ICIAP 2013 - Naples, Italy
    Duration: 9 Sep. 201313 Sep. 2013

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    NumberPART 2
    Volume8157 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference17th International Conference on Image Analysis and Processing, ICIAP 2013
    Country/TerritoryItaly
    CityNaples
    Period9/09/1313/09/13

    Keywords

    • image recognition
    • mixed kernel function
    • pulmonary nodule
    • support vector machine

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