Extraction of Hard Exudates using Functional Link Artificial Neural Networks

dc.contributor.authorBhaskar K.U.en_US
dc.contributor.authorKumar E.P.en_US
dc.date.accessioned2025-02-17T05:23:28Z
dc.date.issued2015
dc.description.abstractOne of the major causes of vision loss is Diabetic Retinopathy (DR). Presence of Hard Exudates (HE) in retinal images is one of the prominent and most reliable symptoms of Diabetic Retinopathy. Thus, it is essential to clinically examine for HEs to perform an early diagnosis and monitoring of DR. In this paper, a classification-based approach using Functional Link Artificial Neural Network (FLANN) classifier to extract HEs in a retinal fundus image is illustrated. Luminosity Contrast Normalization pre-processing step was employed. Classification performances were compared between Multi-Layered Perceptron (MLP), Radial Basis Function (RBF) and FLANN classifiers. Better classification performance was observed for FLANN classifier. GUI package with Region of Interest (ROI) selection tool was developed. � 2015 IEEE.en_US
dc.identifier.citation1en_US
dc.identifier.urihttp://dx.doi.org/10.1109/IADCC.2015.7154742
dc.identifier.urihttps://idr.iitbbs.ac.in/handle/2008/804
dc.language.isoenen_US
dc.subjectClassifieren_US
dc.subjectDiabetic Retinopathyen_US
dc.subjectExudates Detectionen_US
dc.subjectFunctional Link Artificial Neural Network (FLANN)en_US
dc.subjectImage Processingen_US
dc.subjectLuminosity Contrast Normalizationen_US
dc.titleExtraction of Hard Exudates using Functional Link Artificial Neural Networksen_US
dc.typeConference Paperen_US

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