Automatic Screening and Classification of Diabetic Retinopathy Fundus Images

Sarni Suhaila Rahim, Vasile Palade, James Shuttleworth, Chrisina Jayne

    Research output: Contribution to journalArticlepeer-review

    26 Citations (Scopus)

    Abstract

    Eye screening is essential for the early detection and treatment of the diabetic retinopathy. This paper presents an automatic screening system for diabetic retinopathy to be used in the field of retinal ophthalmology. The paper first explores the existing systems and applications related to diabetic retinopathy screening and detection methods that have been previously reported in the literature. The proposed ophthalmic decision support system consists of an automatic acquisition, screening and classification of diabetic retinopathy fundus images, which will assist in the detection and management of the diabetic retinopathy. The developed system contains four main parts, namely the image acquisition, the image preprocessing, the feature extraction, and the classification by using several machine learning techniques.
    Original languageEnglish
    Pages (from-to)113-122
    JournalEngineering Applications of Neural Networks: Communications in Computer and Information Science
    Volume459
    DOIs
    Publication statusPublished - Sept 2014

    Bibliographical note

    This paper is not yet available on the repository. The paper was given at the 15th International Conference on Engineering Applications of Neural Networks, EANN 2014; Sofia; Bulgaria; 5 September 2014 through 7 September 2014

    Keywords

    • Classifiers
    • Diabetic Retinopathy
    • Eye Fundus Images
    • Eye Screening
    • Image Processing

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