Beyond lesion-based diabetic retinopathy: a direct approach for referral

Ramon Pires, Sandra Avila, Herbert F. Jelinek, Jacques Wainer, Eduardo Valle, Anderson Rocha

    Research output: Contribution to journalArticlepeer-review

    55 Citations (Scopus)


    Diabetic retinopathy (DR) is the leading cause of blindness in adults, but can be managed if detected early. Automated DR screening helps by indicating which patients should be referred to the doctor. However, current techniques of automated screening still depend too much on the detection of individual lesions. In this study we bypass lesion detection, and directly train a classifier for DR referral. Additional novelties are the use of state-of-the-art mid-level features for the retinal images: BossaNova and Fisher Vector. Those features extend the classical Bags of Visual Words and greatly improve the accuracy of complex classification tasks. The proposed technique for direct referral is promising, achieving an area under the curve (AUC) of 96.4%, thus, reducing the classification error by almost 40% over the current state of the art, held by lesion-based techniques.
    Original languageEnglish
    Pages (from-to)193-200
    Number of pages8
    JournalIEEE Journal of Biomedical and Health Informatics
    Issue number1
    Publication statusPublished - 2017


    • Bag of Visual Words
    • BossaNova
    • diabetic retinopathy
    • direct referral
    • Fisher Vector
    • referability
    • referral
    • fisher vector
    • Bag of visual words
    • bossanova


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