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• W2804307610 abstract "Diabetic retinopathy damages retina due to diabetes mellitus which leads to blindness. Here, we have applied local binary pattern (LBP) in order to capture the spatial variations of the refractive indices due to progress of diabetic retinopathy among retinal tissues. After extraction of discriminative textures as binary numbers, state of art machine learning algorithms like decision tree and K-NN have been applied to get the optimum detection accuracy in multiclass classifications of in vivo diabetic retinopathy images. Here it is quite apparent that K-NN provides better accuracy and specificity than decision tree." @default.
• W2804307610 created "2018-06-01" @default.
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• W2804307610 date "2018-05-17" @default.
• W2804307610 modified "2023-09-26" @default.
• W2804307610 title "Statistical classifiers on local binary patterns for optical diagnosis of diabetic retinopathy" @default.
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• W2804307610 doi "https://doi.org/10.1117/12.2305447" @default.
• W2804307610 hasPublicationYear "2018" @default.
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