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- W2277033346 abstract "Automatic detection of diabetic retinopathy used in screening systems. In this research a novel splat feature classification method is proposed for the retinal hemorrhage detection to large irregular hemorrhage detection in fundus images. In proposed method retinal color image segmented into non-overlapping segments and each segment contain information about pixels and their spatial locations. A set of features are extracted from each splat to describe its characteristics relative to its surroundings, employs responses from a variety of filter bank, interacting with neighbor splats and shape and their texture information. An optimal subset of splat features are selected by a filter approach followed by a wrapper approach. A FNN classifier is proposed to train with splat-based expert annotations. To improve classification performance with this work FNN classifier is added instead of KNN[1]. A FNN classifier is proposed to train with splat-based expert annotations. FNN method it is based on pixel classification using a feature vector extracted from preprocessed retinal images and given as input to a neural network. A variety of lesion detection tasks can therefore be generalized into exactly the same framework by training classifiers with optimal features learned from available examples projected onto a sub-feature space which maximizes the inter-class distances while minimizes the intra-class distance." @default.
- W2277033346 created "2016-06-24" @default.
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- W2277033346 date "2014-03-01" @default.
- W2277033346 modified "2023-09-27" @default.
- W2277033346 title "RETINAL HEMORRHAGE DETECTION IN DIABETIC RETINOPATHY USING FNN CLASSIFIER" @default.
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