Conference Proceeding
Authors: Lama Seoud (Diagnos Inc.) , Jihed Chelbi (Diagnos Inc.) , Farida Cheriet (Polytechnique Montreal)
With the growing diabetes epidemic, retina specialists have to examine a tremendous amount of fundus images for the detection and grading of diabetic retinopathy. In this study, we propose a first automatic grading system for diabetic retinopathy. First, a red lesion detection is performed to generate a lesion probability map. The latter is then represented by 35 features combining location, size and probability information, which are finally used for classification. A leave-one-out cross-validation using a random forest is conducted on a public database of 1200 images, to classify the images into 4 grades. The proposed system achieved a classification accuracy of 74.1% and a weighted kappa value of 0.731 indicating a significant agreement with the reference. These preliminary results prove that automatic DR grading is feasible, with a performance comparable to that of human experts.
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How to Cite: Seoud, L. , Chelbi, J. & Cheriet, F. (2015) “Automatic Grading of Diabetic Retinopathy on a Public Database”, Proceedings of the Ophthalmic Medical Image Analysis International Workshop. 2(2015). doi: https://doi.org/10.17077/omia.1032