Classification of Diabetic Retinopathy using Machine Learning

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dc.contributorJORGE DE LA CALLEJA MORA;102624es
dc.contributor.advisorDe la Calleja Mora, Jorge
dc.contributor.authorPérez Conde, Pilar
dc.contributor.authorDe la Calleja Mora, Jorge
dc.contributor.authorMedina Nieto, María Auxilio
dc.contributor.authorBenitez Ruiz, Antonio
dc.creatorGUDELIA PILAR PEREZ CONDE;375708es
dc.date.accessioned2018-06-29T05:59:53Z
dc.date.available2018-06-29T05:59:53Z
dc.date.issued2012-01-30
dc.descriptionThis paper presents a method to classify diabetic retinopathy using fundus images. In our study we categorize the disease into two classes: diabetic retinopathy non-proliferative and diabetic retinopathy proliferative. The method reduces the dimensionality of the images and find features using the statistical method of principal component analysis (PCA). Then, we classify the images using decision trees, the naive Bayes classifier, neural networks, k-nearest neighbors and support vector machines. The experimental results show that the naive Bayes classifier obtains the best results with 73.4% of accuracy using a data set of 151 images and testing with different resolutions.es
dc.description.statementofresponsibilityPúblico en generales
dc.identifier.citationPérez C. P., De la Calleja M. J., Medina N. M. A., Benitez R. A. 2012. Classification of Diabetic Retinopathy using Machine Learning. Universidad Politécnica de Puebla. Revista Visión Politécnica. Número 1.es
dc.identifier.urihttp://repositorio.uppuebla.edu.mx:8080/xmlui/handle/123456789/204
dc.languageIngléses
dc.publisherUNIVERSIDAD POLITÉCNICA DE PUEBLAes
dc.relationVersión del autores
dc.relation.ispartofREPOSITORIO NACIONAL CONACYTes
dc.rightsAcceso Abiertoes
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0es
dc.subjectMedical Image Analysis, Machine Learning, Principal Analysis Component, Diabetic Retinopathyes
dc.subject.classificationINGENIERÍA Y TECNOLOGÍAes
dc.titleClassification of Diabetic Retinopathy using Machine Learninges
dc.typeArtículoes

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