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• W2903443739 abstract "The most prominent ophthalmic cause of blindness is Diabetic Retinopathy (DR). This retinal disease is characterized by variation in diameter of the retinal blood vessel and the new blood vessel growth inside the retina. A system to enhance the quality of the segmentation result over the pathological retinal images has been proposed. The proposed method uses Contrast Limited Adaptive Histogram Equalization (CLAHE) for preprocessing and Tandem Pulse Coupled Neural Network (TPCNN) model for automatic feature vectors generation then classification and extraction of the retinal blood vessels via Deep Learning Based Support Vector Machine (DLBSVM). The proposed approach is assessed over the standard public fundus image databases to evaluate the performance. The results render that these techniques improve the segmentation results with an average value of 74.45% sensitivity, 99.40% specificity, and 99.16% accuracy. The results evoke that the proposed method is a suitable alternative for supervised techniques." @default.
• W2903443739 created "2018-12-11" @default.
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• W2903443739 date "2019-01-01" @default.
• W2903443739 modified "2023-10-03" @default.
• W2903443739 title "Segmentation of retinal blood vessels from ophthalmologic Diabetic Retinopathy images" @default.
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• W2903443739 doi "https://doi.org/10.1016/j.compeleceng.2018.11.024" @default.
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