Automatic retinopathy detection using Deep learning and medical findings

ilustraciones, gráficas, tablas

Autores:: de la Pava Rodriguez, Melissa

Tipo de recurso:

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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oai_identifier_str	oai:repositorio.unal.edu.co:unal/81038
network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.eng.fl_str_mv	Automatic retinopathy detection using Deep learning and medical findings
dc.title.translated.spa.fl_str_mv	Detección automática de retinopatía diabética usando aprendizaje profundo y hallazgos médicos
title	Automatic retinopathy detection using Deep learning and medical findings
spellingShingle	Automatic retinopathy detection using Deep learning and medical findings 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Diabetic Retinopathy/diagnosis Deep Learning Machine learning Retinopatía Diabética/diagnóstico Aprendizaje Profundo Aprendizaje Automático Ocular lesions Diabetic retinopathy Convolutional neural networks Transfer learning Multitask models Shallow machine learning classifiers Lesiones oculares Retinopatía diabética Redes convolucionales Transferecia de aprendizaje Modelo multitarea Clasificadores clásicos de aprendizaje de máquina
title_short	Automatic retinopathy detection using Deep learning and medical findings
title_full	Automatic retinopathy detection using Deep learning and medical findings
title_fullStr	Automatic retinopathy detection using Deep learning and medical findings
title_full_unstemmed	Automatic retinopathy detection using Deep learning and medical findings
title_sort	Automatic retinopathy detection using Deep learning and medical findings
dc.creator.fl_str_mv	de la Pava Rodriguez, Melissa
dc.contributor.advisor.spa.fl_str_mv	González Osorio, Fabio Augusto Perdomo Charry, Oscar Julián
dc.contributor.author.spa.fl_str_mv	de la Pava Rodriguez, Melissa
dc.contributor.researchgroup.spa.fl_str_mv	MindLab
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
topic	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Diabetic Retinopathy/diagnosis Deep Learning Machine learning Retinopatía Diabética/diagnóstico Aprendizaje Profundo Aprendizaje Automático Ocular lesions Diabetic retinopathy Convolutional neural networks Transfer learning Multitask models Shallow machine learning classifiers Lesiones oculares Retinopatía diabética Redes convolucionales Transferecia de aprendizaje Modelo multitarea Clasificadores clásicos de aprendizaje de máquina
dc.subject.decs.eng.fl_str_mv	Diabetic Retinopathy/diagnosis Deep Learning Machine learning
dc.subject.decs.spa.fl_str_mv	Retinopatía Diabética/diagnóstico Aprendizaje Profundo Aprendizaje Automático
dc.subject.proposal.eng.fl_str_mv	Ocular lesions Diabetic retinopathy Convolutional neural networks Transfer learning Multitask models Shallow machine learning classifiers
dc.subject.proposal.spa.fl_str_mv	Lesiones oculares Retinopatía diabética Redes convolucionales Transferecia de aprendizaje Modelo multitarea Clasificadores clásicos de aprendizaje de máquina
description	ilustraciones, gráficas, tablas
publishDate	2021
dc.date.issued.none.fl_str_mv	2021
dc.date.accessioned.none.fl_str_mv	2022-02-22T16:35:41Z
dc.date.available.none.fl_str_mv	2022-02-22T16:35:41Z
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/81038
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/81038 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computación
dc.publisher.department.spa.fl_str_mv	Departamento de Ingeniería de Sistemas e Industrial
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingeniería
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
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spelling	Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2González Osorio, Fabio Augusto35912f60905ba6e179208c70e6024e80Perdomo Charry, Oscar Julián0257d02fec95a5e32cb46abe673774b2de la Pava Rodriguez, Melissaeee6c1425b69725c95762d016ba68de7MindLab2022-02-22T16:35:41Z2022-02-22T16:35:41Z2021https://repositorio.unal.edu.co/handle/unal/81038Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficas, tablasDiabetic retinopathy (DR) is the result of a complication of diabetes affecting the retina. It can cause blindness if left undiagnosed and untreated. The ophthalmologist performs the diagnosis by screening each patient and detecting in ocular imaging the lesions caused by DR, namely, microaneurisms, hemorrhages, cotton wool spots, venous beading and neovascularization. However, the analysis of ocular findings is cumbersome, time-consuming, and demanding. Due to the insufficient amount of trained specialists to diagnose the illness, and the actual growing population with DR, it is important to develop a method to assist the DR diagnosis. This thesis presents two approaches for the automatic classification of DR using eye fundus images. The first one utilizes convolutional neural networks, transfer learning and shallow machine learning classifiers to identify the main ocular lesions related to DR and then use them to diagnose the illness. The second one is a multitask model which predicts simultaneously ocular lesions and DR. These approaches follow a similar workflow to that of clinicians, providing information that can be interpreted clinically to support the prediction. To achieve this goal a subset of the kaggle EyePACS and the Messidor-2 datasets, are labeled with ocular lesions by a certified opthalmologist. The kaggle EyePACS subset is used as training set and the Messidor-2 dataset is used as test set for both, the lesions and DR classification models. The results indicate that both methods achieve results comparable with state-of-the-art performances. The best results are obtained using the first approach with a multi layer perceptron as classifier for the automatic detection of DR, however, the multitask approach lead to similar results and has a simpler architecture.La retinopatía diabética (RD) es el resultado de una complicacion de la diabetes que afecta la retina. Puede causar ceguera si no se diagnostica ni se trata. El diagnóstico de esta enfermedad se hace mediante el escaneo de cada paciente y el análisis de imágenes oculares para detectar lesiones causadas por la RD, como microaneurismas, hemorragias, manchas algodonosas, arrosamiento venoso y neovascularización. Sin embargo, el análisis de las lesiones oculares es engorroso, lento y exigente. Debido a la cantidad insuficiente de especialistas capacitados para diagnosticar la enfermedad y al crecimiento actual de la población con RD, es importante desarrollar un método para ayudar en el diagnóstico de esta enfermedad. Esta tesis presenta dos enfoques para la clasificación automática de la RD utilizando imágenes de fondo de ojo. El primero utiliza redes neuronales convolucionales, transferencia de aprendizaje y clasificadores clásicos de aprendizaje de máquina para identificar las principales lesiones oculares relacionadas con la RD y luego usarlas para diagnosticar la enfermedad. El segundo es un modelo multitarea que predice simultáneamente lesiones oculares y RD. Estos enfoques siguen un flujo de trabajo similar al de los médicos, proporcionando información que puede interpretarse clínicamente para respaldar la predicción. Para lograr este objetivo, un subconjunto de las bases de datos kaggle EyePACS y Messidor-2 fueron etiquetados con lesiones oculares por un oftalmólogo certificado. El subconjunto de kaggle EyePACS se utiliza como conjunto de entrenamiento y el de Messidor-2 se utiliza como conjunto de prueba tanto para los modelos de detección de lesiones, como para los de clasificación de RD. Los resultados indican que ambos enfoques logran desempeños comparables con los métodos del estado del arte. Los mejores resultados se obtienen utilizando el primer enfoque con un perceptrón multicapa como clasificador para la detección automática de RD, sin embargo, el enfoque multitarea conduce a resultados similares y tiene una arquitectura más simple. (Texto tomado de la fuente).Incluye anexosMaestríaMagíster en Ingeniería - Ingeniería de Sistemas y ComputaciónApplied computingx, 52 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y ComputaciónDepartamento de Ingeniería de Sistemas e IndustrialFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresDiabetic Retinopathy/diagnosisDeep LearningMachine learningRetinopatía Diabética/diagnósticoAprendizaje ProfundoAprendizaje AutomáticoOcular lesionsDiabetic retinopathyConvolutional neural networksTransfer learningMultitask modelsShallow machine learning classifiersLesiones ocularesRetinopatía diabéticaRedes convolucionalesTransferecia de aprendizajeModelo multitareaClasificadores clásicos de aprendizaje de máquinaAutomatic retinopathy detection using Deep learning and medical findingsDetección automática de retinopatía diabética usando aprendizaje profundo y hallazgos médicosTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAutomatic Detection of Hard Exudates in Color Retinal Images Using Dynamic Threshold and SVM Classification: Algorithm Development and Evaluation. 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Automatic retinopathy detection using Deep learning and medical findings

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