• DocumentCode
    693802
  • Title

    Age-Related Macular Degeneration Screening Using Data Mining Approaches

  • Author

    Hijazi, Mohd Hanafi Ahmad ; Coenen, Frans ; Yalin Zheng

  • Author_Institution
    Appl. Comput. Group, Univ. Malaysia Sabah, Kota Kinabalu, Malaysia
  • fYear
    2013
  • fDate
    3-5 Dec. 2013
  • Firstpage
    299
  • Lastpage
    303
  • Abstract
    This paper investigates the use of tabular approach to classifying retinal images according to whether they feature Age-related Macular Degeneration (AMD), a retina condition that causes blindness in old age. The novelty of the proposed approach is that it is not founded on feature segmentation, instead entire image encodings are used. Features in the form of statistical parameters extracted directly and indirectly from the images are considered. For the evaluation two publically available, retinal fundus image data sets were used. The evaluation was conducted in the context of AMD screening. Excellent results were produced: Sensitivity and AUC of 90% and over were recorded for binary-class classification problem.
  • Keywords
    data mining; eye; feature extraction; image classification; image coding; statistical analysis; AMD screening; AUC; age-related macular degeneration screening; binary-class classification problem; blindness; data mining approaches; feature extraction; image encodings; old age person; publically available retinal fundus image data sets; retina condition; retinal image classification; sensitivity; statistical parameters; tabular approach; Biomedical imaging; Feature extraction; Histograms; Image color analysis; Image segmentation; Retina; Sensitivity; Age-related Macular Degeneration; Classification; Data Mining; Decision Support Techniques; Retinal Image;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Artificial Intelligence, Modelling and Simulation (AIMS), 2013 1st International Conference on
  • Conference_Location
    Kota Kinabalu
  • Print_ISBN
    978-1-4799-3250-4
  • Type

    conf

  • DOI
    10.1109/AIMS.2013.55
  • Filename
    6959933