• DocumentCode
    1776339
  • Title

    Automatic detection of lung nodules using classifiers

  • Author

    Thomas, Renu Ann ; Kumar, Sahoo Subhendu

  • Author_Institution
    Control & Instrum., Noorul Islam Univ., Thuckalay, India
  • fYear
    2014
  • fDate
    10-11 July 2014
  • Firstpage
    705
  • Lastpage
    710
  • Abstract
    In this paper, comparison between three classifiers for lung cancer diagnosis is proposed. Morphological Operations is used for preprocessing of the images and gray level cooccurrence matrix is used for the feature extraction process and SVM, Minimum distance and k-nearest neighbor classifiers are used for classification. Experimental analysis is made with data set to evaluate the performance of the different classifiers. The performance of SVM classifiers is found to be the best based correct and incorrect classification of the classifier.
  • Keywords
    cancer; feature extraction; image classification; image segmentation; lung; matrix algebra; medical image processing; object detection; support vector machines; SVM classifiers; automatic lung nodule detection; feature extraction process; gray level cooccurrence matrix; image preprocessing; k-nearest neighbor classifiers; lung cancer diagnosis; minimum distance classifiers; morphological operations; Accuracy; Cancer; Feature extraction; Image segmentation; Instruments; Lungs; Support vector machines; Classifiers; Preprocssing; Segmentation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 2014 International Conference on
  • Conference_Location
    Kanyakumari
  • Print_ISBN
    978-1-4799-4191-9
  • Type

    conf

  • DOI
    10.1109/ICCICCT.2014.6993051
  • Filename
    6993051