• DocumentCode
    152423
  • Title

    Performance of ensemble learning classifiers on malignant-benign classification of pulmonary nodules

  • Author

    Tartar, A. ; Akan, A.

  • Author_Institution
    Muhendislik Bilimleri Bolumu, Istanbul Univ., İstanbul, Turkey
  • fYear
    2014
  • fDate
    23-25 April 2014
  • Firstpage
    722
  • Lastpage
    725
  • Abstract
    Computer-aided detection systems can help radiologists to detect pulmonary nodules at an early stage. In this study, a novel Computer-aided Diagnosis system (CAD) is proposed for the classification of pulmonary nodules as malignant and benign. Proposed CAD system, providing an important support to radiologists at the diagnosis process of the disease, achieves high classification performance using ensemble learning classifiers.
  • Keywords
    diseases; learning (artificial intelligence); medical diagnostic computing; pattern classification; CAD system; computer-aided detection systems; computer-aided diagnosis system; disease diagnosis process; ensemble learning classifiers; high classification performance; malignant-benign classification; pulmonary nodule detection; Bagging; Biomedical imaging; Cancer; Computer aided diagnosis; Conferences; Lungs; Signal processing; computer-aided diagnosis system; ensemble learning classifiers; malignant-benign classification; pulmonary nodules;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing and Communications Applications Conference (SIU), 2014 22nd
  • Conference_Location
    Trabzon
  • Type

    conf

  • DOI
    10.1109/SIU.2014.6830331
  • Filename
    6830331
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=152423