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dc.contributor.authorAsha P R-
dc.contributor.authorKarpagavalli S-
dc.date.accessioned2020-10-13T08:48:08Z-
dc.date.available2020-10-13T08:48:08Z-
dc.date.issued2015-01-05-
dc.identifier.isbn978-1-4799-6438-3-
dc.identifier.other10.1109/ICACCS.2015.7324057-
dc.identifier.urihttps://ieeexplore.ieee.org/document/7324057-
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/2197-
dc.description.abstractDiabetic Retinopathy (DR) is an eye filled illness caused by the complication of polygenic disease and that is to be detected accurately for timely treatment. As polygenic disease progresses, the vision of a patient could begin to deteriorate and leads to blindness. In this proposed work, the presence or absence of retinal exudates are detected using machine learning (ML) techniques. To detect the presence of exudates features like Mean, Standard deviation, Centroid and Edge Strength are extracted from Luv color space after segmenting the Retinal image. A total of 100 images were used, out of which 80 images were used for training and 20 images were used for testing. The classification task carried out with classifiers like Naive bayes (NB), Multilayer Perceptron (MLP) and Extreme Learning Machine (ELM). Experimental results shows that the model built using Extreme Learning Machine outperforms other two models and effectively detects the presence of exudates in retinal images.en_US
dc.language.isoenen_US
dc.publisherInternational Conference on Advanced Computing & Communication Systems, held at Sri Eshwar College of Engineering, Coimbatore during 5-7 January 2015 and published in the conference proceedings, indexed in IEEE Xplore Digital Library.en_US
dc.subjectDiabetic retinopathyen_US
dc.subjectExtreme Learning Machineen_US
dc.subjectExudates color spaceen_US
dc.subjectfuzzy C-meansen_US
dc.subjecthistogram specificationen_US
dc.titleDIABETIC RETINAL EXUDATES DETECTION USING MACHINE LEARNING TECHNIQUESen_US
dc.typeBooken_US
Appears in Collections:International Conference

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