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- W2783257414 abstract "Diabetic Macular Edema affects vision and eventually may lead to blindness. Early detection is vital to prevent the ramifications of the disease requiring the need for effective computer-aided diagnostic techniques. A robust method is proposed in the following paper to segment exudates from fundus images using a support vector machine for classification. This is followed by detection of macula using morphological features and comparing the segmented exudates against specially created regions around the macula. The algorithm further grades the image according to how severe the disease is. The proposed algorithm has been tested on 89 images of DIARETDB1 database and gives an accuracy of 92.11% in detecting severe case of DME and 90%accuracy for the same when tested on 100 images of MESSIDOR database. The proposed method can be used to directly give an idea about the severity of Diabetic Macular Edema in the image ad give a reliable and cost-effective diagnosis in real time." @default.
- W2783257414 created "2018-01-26" @default.
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- W2783257414 date "2017-10-01" @default.
- W2783257414 modified "2023-10-16" @default.
- W2783257414 title "Automated detection and grading of diabetic macular edema from digital colour fundus images" @default.
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- W2783257414 doi "https://doi.org/10.1109/upcon.2017.8251096" @default.
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