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Deep learning approach to identify diseases and biomarkers in optical coherence tomography scans

dc.rights.licenseAtribución-NoComercial-CompartirIgual 4.0 Internacional
dc.contributor.advisorGonzález Osorio, Fabio Augusto
dc.contributor.advisorPerdomo Charry, Oscar Julian
dc.contributor.authorSánchez Legarda, Yeison David
dc.date.accessioned2022-03-03T16:30:51Z
dc.date.available2022-03-03T16:30:51Z
dc.date.issued2021
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/81123
dc.descriptionilustraciones, gráficas, tablas
dc.description.abstractThe most common causes of blindness around the world are retinal diseases, to identify them and not allow to lead to loss of vision for a person an early diagnosis is necessary, nowadays the use of OCT scans to perform this diagnostic has increased due to the capacity to show in detail biomarkers as fluids, drusen, cyst and hyperreflective foci. However the OCT scans analysis is not easy and time consuming even for experts ophthalmologist and in combination with the overload work overload in the healthcare system makes even more difficult to diagnose and follow-up the retinal disease, at this point comes in to help deep learning allowing the automated detection of diseases and biomarkers, With the thesis work “Deep Learning Approach to Identify Diseases and Biomarkers in Optical Coherence Tomography Scans," a method was proposed to OCT scans segmentation to obtain biomarkers which can help the ophthalmologist to check response to treatment or identify a retinal disease, furthermore a deep learning method for check which disease is present in a scan was implemented.
dc.description.abstractLas causas más comunes de ceguera en todo el mundo son las enfermedades de la retina, para identificarlas y no permitir que lleven a la pérdida de la visión de una persona es necesario un diagnóstico temprano, hoy en día el uso de las imágenes OCT para realizar este diagnóstico se ha incrementado debido a la capacidad de mostrar en detalle biomarcadores como fluidos, drusas, quistes y focos hiperreflectivos, sin embargo el análisis de las imágenes OCT no es fácil y consume mucho tiempo incluso para los oftalmólogos expertos lo que combinado con la sobrecarga de trabajo en el sistema de salud hace aún más difícil el diagnóstico y seguimiento de las enfermedades retinales, Con el trabajo de tesis "Deep Learning Approach to Identify Diseases and Biomarkers in Optical Coherence Tomography Scans", se propone un método para la segmentación de imágenes OCT con el fin de obtener biomarcadores que puedan ayudar al oftalmólogo a comprobar la respuesta al tratamiento o identificar una enfermedad de la retina, además se implementó un método de aprendizaje profundo para comprobar qué enfermedad está presente en una imagen. (Texto tomado de la fuente).
dc.format.extentxii, 56 páginas
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherUniversidad Nacional de Colombia
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.subject.ddc000 - Ciencias de la computación, información y obras generales
dc.titleDeep learning approach to identify diseases and biomarkers in optical coherence tomography scans
dc.typeTrabajo de grado - Maestría
dc.type.driverinfo:eu-repo/semantics/masterThesis
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.publisher.programBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computación
dc.contributor.researchgroupMindlab
dc.description.degreelevelMaestría
dc.description.degreenameMagíster en Ingeniería - Ingeniería de Sistemas y Computación
dc.description.researchareaDeep learning
dc.description.researchareaComputer vision
dc.description.researchareaMachine learning
dc.identifier.instnameUniversidad Nacional de Colombia
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourlhttps://repositorio.unal.edu.co/
dc.publisher.departmentDepartamento de Ingeniería de Sistemas e Industrial
dc.publisher.facultyFacultad de Ingeniería
dc.publisher.placeBogotá, Colombia
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotá
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.subject.decsDeep Learning
dc.subject.decsAprendizaje profundo
dc.subject.decsEnfermedades de la Retina
dc.subject.decsRetinal Diseases
dc.subject.decsTomography, Optical
dc.subject.decsTomografía Óptica
dc.subject.proposalComputer vision
dc.subject.proposalAprendizaje profundo
dc.subject.proposalDeep learning
dc.subject.proposalMachine learning
dc.subject.proposalOptical coherence tomography scans
dc.subject.proposalBiomarkers segmentation
dc.subject.proposalRetinal diseases classification
dc.subject.proposalTomografía de coherencia óptica
dc.subject.proposalVisión por computador
dc.subject.proposalAprendizaje de máquinas
dc.subject.proposalSegmentación de biomarcadores
dc.subject.proposalClasificación de enfermedades retinianas
dc.subject.proposalRedes neuronales generativas adversarias
dc.title.translatedEnfoque de aprendizaje profundo para identificar enfermedades y biomarcadores en imagenes de tomografía de coherencia óptica
dc.type.coarhttp://purl.org/coar/resource_type/c_bdcc
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.contentText
dc.type.redcolhttp://purl.org/redcol/resource_type/TM
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2
dcterms.audience.professionaldevelopmentEstudiantes
dcterms.audience.professionaldevelopmentInvestigadores
dcterms.audience.professionaldevelopmentMaestros


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