Automatic grading of diabetic retinopathy on a public database

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Proceedings titleProceedings of the Ophthalmic Medical Image Analysis Second International Workshop (OMIA 2015)
ConferenceOphthalmic Medical Image Analysis Second International Workshop, OMIA 2015, held in conjunction with MICCAI 2015, October 9, 2015, Munich, Germany
AbstractWith 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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AffiliationNational Research Council Canada
Peer reviewedYes
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This is a non-NRC publication

"Non-NRC publications" are publications authored by NRC employees prior to their employment by NRC.

NPARC number23000999
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Record identifier173fd72d-1281-4c07-b26b-35fc38aa8ef5
Record created2016-11-25
Record modified2016-11-25
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