17 May 2018 Statistical classifiers on local binary patterns for optical diagnosis of diabetic retinopathy
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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.
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Sabyasachi Mukhopadhyay, Sabyasachi Mukhopadhyay, Sawon Pratiher, Sawon Pratiher, Sukanya Mukherjee, Sukanya Mukherjee, Gautham Pasupuleti, Gautham Pasupuleti, Ritwik Barman, Ritwik Barman, Jay Chhablani, Jay Chhablani, Prasanta K. Panigrahi, Prasanta K. Panigrahi, } "Statistical classifiers on local binary patterns for optical diagnosis of diabetic retinopathy ", Proc. SPIE 10685, Biophotonics: Photonic Solutions for Better Health Care VI, 106852Y (17 May 2018); doi: 10.1117/12.2305447; https://doi.org/10.1117/12.2305447
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