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

54 Scopus citations


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 of 96.4%, thus, reducing the classification error by almost 40% over the current state of the art, held by lesion-based techniques.

Original languageBritish English
Pages (from-to)193-200
Number of pages8
JournalIEEE Journal of Biomedical and Health Informatics
Issue number1
StatePublished - Jan 2017


  • Bag of visual words
  • bossanova
  • diabetic retinopathy
  • direct referral
  • fisher vector
  • referability
  • referral


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