Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/116105
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dc.creatorPedro Filipe Cavaleiro Breda
dc.date.accessioned2019-02-04T07:53:40Z-
dc.date.available2019-02-04T07:53:40Z-
dc.date.issued2018-09-19
dc.date.submitted2018-10-17
dc.identifier.othersigarra:292224
dc.identifier.urihttps://repositorio-aberto.up.pt/handle/10216/116105-
dc.descriptionThe main goal of this dissertation is to study and analyze different approaches based on deep learning techniques for the segmentation of retinal blood vessels. In order to do so, different design and architectures of CNN's will be studied and analysed, as their results and performance are evaluated and compared with the available algorithms. One other important objective of this work is to study and evaluate the different techniques that have been used for vessel segmentation, such as machine learning, and how these can be combined with the deep learning approaches. By Analyzing the features that the learned models are using to perform classification and combining them with different machine learning techniques (such as Random Forest and SVM Classifiers), another goal is to proposed a solution or set of solutions to perform the retinal vessel segmentation.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectEngenharia electrotécnica, electrónica e informática
dc.subjectElectrical engineering, Electronic engineering, Information engineering
dc.titleDeep Learning for the Segmentation of Vessels in Retinal Fundus images and its Interpretation
dc.typeDissertação
dc.contributor.uportoFaculdade de Engenharia
dc.subject.fosCiências da engenharia e tecnologias::Engenharia electrotécnica, electrónica e informática
dc.subject.fosEngineering and technology::Electrical engineering, Electronic engineering, Information engineering
thesis.degree.disciplineMestrado Integrado em Engenharia Electrotécnica e de Computadores
thesis.degree.grantorFaculdade de Engenharia
thesis.degree.grantorUniversidade do Porto
thesis.degree.level1
Appears in Collections:FEUP - Dissertação

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