2019
DOI: 10.1007/s10916-019-1349-7
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Detection of Hard Exudates Using Evolutionary Feature Selection in Retinal Fundus Images

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Cited by 16 publications
(6 citation statements)
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“…Relevant features of an entity can accurately identify it. Exudates can be greatly recognized by its shape, size, color, texture, intensity and edge strength [8], [38], [55], [44]. Detection of all lesions [46], [58] and multiple DR stages [47], [54] is the matter of concern.…”
Section: Discussionmentioning
confidence: 99%
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“…Relevant features of an entity can accurately identify it. Exudates can be greatly recognized by its shape, size, color, texture, intensity and edge strength [8], [38], [55], [44]. Detection of all lesions [46], [58] and multiple DR stages [47], [54] is the matter of concern.…”
Section: Discussionmentioning
confidence: 99%
“…Though, investigation in this realm has reached a far way. Still, there is a need to focus on some areas like discriminating between hard and soft exudates [35], better feature selection [55] and improvement in OD and blood vessel detection [49].…”
Section: Discussionmentioning
confidence: 99%
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“…The structure of the neural network comprises of three layers: input, hidden, and output layer. [26][27] The number of neurons in the input layer is selected based on the number of features extracted using wavelets in the extraction phase. 28 The hidden layer is designed to have 30 neurons, and the output layer shall predict whether the given mammogram is benign or malignant.…”
Section: Artificial Neural Networkmentioning
confidence: 99%
“…There are many surveys already published in this study like Dimple Nagpal [ 126 ], et al, 2021 has published a review paper describing the approaches, datasets and performance measures of diabetic retinopathy. Anoop Balakrishnan and Subbian [ 81 ] has given an overview of different diabetic retinopathy detection techniques using machine learning in fundus images but fails to review even about some of the basic deep learning algorithms which are nowadays commonly used in the DR detection [ 13 ]. Moreover, Norah Asiri, et al, considers the different deep learning-based algorithms and also describes the different datasets used in the DR detection.…”
Section: Introductionmentioning
confidence: 99%