Automatic identification of diabetic maculopathy stages using fundus images

J. Nayak, P. S. Bhat, U. R. Acharya

Research output: Contribution to journalArticlepeer-review

42 Citations (Scopus)


Diabetes mellitus is a major cause of visual impairment and blindness. Twenty years after the onset of diabetes, almost all patients with type 1 diabetes and over 60% of patients with type 2 diabetes will have some degree of retinopathy. Prolonged diabetes retinopathy leads to maculopathy, which impairs the normal vision depending on the severity of damage of the macula. This paper presents a computer-based intelligent system for the identification of clinically significant maculopathy, non-clinically significant maculopathy and normal fundus eye images. Features are extracted from these raw fundus images which are then fed to the classifier. Our protocol uses feed-forward architecture in an artificial neural network classifier for classification of different stages. Three different kinds of eye disease conditions were tested in 350 subjects. We demonstrated a sensitivity of more than 95% for these classifiers with a specificity of 100%, and results are very promising. Our systems are ready to run clinically on large amounts of datasets.

Original languageEnglish
Pages (from-to)119-129
Number of pages11
JournalJournal of Medical Engineering and Technology
Issue number2
Publication statusPublished - 01-02-2009
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering


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