TY - CONF AB - <p>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.</p> AU - Lama Seoud, Jihed Chelbi, Farida Cheriet DA - 2015/10// DO - 10.17077/omia.1032 IS - 2015 VL - 2 PB - University of Iowa PY - 2015 TI - Automatic Grading of Diabetic Retinopathy on a Public Database T2 - Proceedings of the Ophthalmic Medical Image Analysis International Workshop UR - https://pubs.lib.uiowa.edu/omia/article/id/27673/ ER -