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dc.contributor.authorMolina-Cabello, Miguel Ángel
dc.contributor.authorLópez-Rubio, Ezequiel 
dc.contributor.authorLuque-Baena, Rafael Marcos
dc.contributor.authorRodríguez-Espinosa, María Jesús
dc.contributor.authorThurnhofer-Hemsi, Karl
dc.date.accessioned2018-04-16T09:02:59Z
dc.date.available2018-04-16T09:02:59Z
dc.date.issued2018
dc.identifier.urihttps://hdl.handle.net/10630/15526
dc.descriptionhttps://doi.org/10.1007/978-3-319-77712-2_62en_US
dc.description.abstractThe detection of red blood cells in blood samples can be crucial for the disease detection in its early stages. The use of image processing techniques can accelerate and improve the effectiveness and efficiency of this detection. In this work, the use of the Circle Hough transform for cell detection and artificial neural networks for their identification as a red blood cell is proposed. Specifically, the application of neural networks (MLP) as a standard classification technique with (MLP) is compared with new proposals related to deep learning such as convolutional neural networks (CNNs). The different experiments carried out reveal the high classification ratio and show promising results after the application of the CNNs.en_US
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectRedes neuronales (Informática)en_US
dc.subjectCélulas sanguíneas - Clasificaciónen_US
dc.subject.otherBlood cell detectionen_US
dc.subject.otherBlood cell classificationen_US
dc.subject.otherCircle Hough transformen_US
dc.subject.otherConvolutional neural networksen_US
dc.titleBlood Cell Classification Using the Hough Transform and Convolutional Neural Networksen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.centroE.T.S.I. Informáticaen_US
dc.relation.eventtitleWorldCist'18 - 6th World Conference on Information Systems and Technologiesen_US
dc.relation.eventplaceNápoles, Italiaen_US
dc.relation.eventdate27 al 29 de marzo de 2018en_US


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