Modelo de aprendizaje profundo para cuantificar el daño causado por el Glaucoma en el nervio óptico
ilustraciones, fotografías, graficas
- Autores:
-
Beltrán Barrera, Lillian Daniela
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/83865
- Palabra clave:
- 000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación
Enfermedades del Nervio Óptico
Optic Nerve Diseases
Glaucoma
Escala DDLS
RDR
YOLO
Aprendizaje por transferencia
Redes neuronales convolucionales
Modelo de clasificación
Modelo de segmentación
Glaucoma
DDLS scale
RDR
YOLO
Transfer learning
Convolutional neural networks
Classification model
Segmentation model
Modelo de simulación
Simulation models
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Modelo de aprendizaje profundo para cuantificar el daño causado por el Glaucoma en el nervio óptico |
| dc.title.translated.eng.fl_str_mv |
Deep learning model to quantify the damage caused by Glaucoma in the optic nerve |
| title |
Modelo de aprendizaje profundo para cuantificar el daño causado por el Glaucoma en el nervio óptico |
| spellingShingle |
Modelo de aprendizaje profundo para cuantificar el daño causado por el Glaucoma en el nervio óptico 000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación Enfermedades del Nervio Óptico Optic Nerve Diseases Glaucoma Escala DDLS RDR YOLO Aprendizaje por transferencia Redes neuronales convolucionales Modelo de clasificación Modelo de segmentación Glaucoma DDLS scale RDR YOLO Transfer learning Convolutional neural networks Classification model Segmentation model Modelo de simulación Simulation models |
| title_short |
Modelo de aprendizaje profundo para cuantificar el daño causado por el Glaucoma en el nervio óptico |
| title_full |
Modelo de aprendizaje profundo para cuantificar el daño causado por el Glaucoma en el nervio óptico |
| title_fullStr |
Modelo de aprendizaje profundo para cuantificar el daño causado por el Glaucoma en el nervio óptico |
| title_full_unstemmed |
Modelo de aprendizaje profundo para cuantificar el daño causado por el Glaucoma en el nervio óptico |
| title_sort |
Modelo de aprendizaje profundo para cuantificar el daño causado por el Glaucoma en el nervio óptico |
| dc.creator.fl_str_mv |
Beltrán Barrera, Lillian Daniela |
| dc.contributor.advisor.none.fl_str_mv |
Perdomo Charry, Oscar Julian Gonzalez Osorio, Fabio Augusto |
| dc.contributor.author.none.fl_str_mv |
Beltrán Barrera, Lillian Daniela |
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Mindlab |
| dc.subject.ddc.spa.fl_str_mv |
000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación |
| topic |
000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación Enfermedades del Nervio Óptico Optic Nerve Diseases Glaucoma Escala DDLS RDR YOLO Aprendizaje por transferencia Redes neuronales convolucionales Modelo de clasificación Modelo de segmentación Glaucoma DDLS scale RDR YOLO Transfer learning Convolutional neural networks Classification model Segmentation model Modelo de simulación Simulation models |
| dc.subject.decs.spa.fl_str_mv |
Enfermedades del Nervio Óptico |
| dc.subject.decs.eng.fl_str_mv |
Optic Nerve Diseases |
| dc.subject.proposal.spa.fl_str_mv |
Glaucoma Escala DDLS RDR YOLO Aprendizaje por transferencia Redes neuronales convolucionales Modelo de clasificación Modelo de segmentación |
| dc.subject.proposal.eng.fl_str_mv |
Glaucoma DDLS scale RDR YOLO Transfer learning Convolutional neural networks Classification model Segmentation model |
| dc.subject.unesco.none.fl_str_mv |
Modelo de simulación Simulation models |
| description |
ilustraciones, fotografías, graficas |
| publishDate |
2023 |
| dc.date.accessioned.none.fl_str_mv |
2023-05-25T15:38:35Z |
| dc.date.available.none.fl_str_mv |
2023-05-25T15:38:35Z |
| dc.date.issued.none.fl_str_mv |
2023-04 |
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Trabajo de grado - Maestría |
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info:eu-repo/semantics/masterThesis |
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info:eu-repo/semantics/acceptedVersion |
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Text |
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http://purl.org/redcol/resource_type/TM |
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acceptedVersion |
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https://repositorio.unal.edu.co/handle/unal/83865 |
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Universidad Nacional de Colombia |
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Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
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https://repositorio.unal.edu.co/handle/unal/83865 https://repositorio.unal.edu.co/ |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
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spa |
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Abdel-Mottaleb, “A Comparative Study of Deep Learning Models for Diagnosing Glaucoma From Fundus Images,” IEEE Access, vol. 9, pp. 23 894–23 906, 2021. [23] A. M. Stefan, E. A. Paraschiv, S. Ovreiu, and E. Ovreiu, “A review of glaucoma detec- tion from digital fundus images using machine learning techniques,” 2020 8th E-Health and Bioengineering Conference, EHB 2020, pp. 20–23, 2020. [24] D. Mirzania, A. C. Thompson, and K. W. Muir, “Applications of deep learning in detection of glaucoma: A systematic review,” European Journal of Ophthalmology, 2020. |
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Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Perdomo Charry, Oscar Julianc280ba13fd48e8dbf9cdbc8179aa9c94Gonzalez Osorio, Fabio Augusto0e9d70b5c1d7448338ca4467ccb27e59Beltrán Barrera, Lillian Danielaeb0767c0c25ee22af0721c61a7542ba0Mindlab2023-05-25T15:38:35Z2023-05-25T15:38:35Z2023-04https://repositorio.unal.edu.co/handle/unal/83865Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, fotografías, graficasEl glaucoma es una de las enfermedades de mayor prevalencia y gravedad en el mundo, se caracteriza por provocar una pérdida gradual de la visión periférica, que si no se trata a tiempo, puede ser irreversible y conducir a la pérdida total de la visión. Con el objetivo de facilitar la detección temprana de esta enfermedad, se han propuesto diversos modelos basados en aprendizaje profundo y redes neuronales convolucionales que permiten un diagnóstico automatizado. A pesar de su utilidad, estos modelos presentan algunas limitaciones, como la evaluación del ancho del borde neurorretiniano solamente de forma vertical y la asignación de una clasificación binaria para denotar la presencia o ausencia de la enfermedad, lo que dificulta la identificación de su estadio y del avance de la enfermedad en múltiples direcciones. Por tal motivo, este trabajo presenta un enfoque basado en aprendizaje profundo que toma como referencia la escala DDLS (Disc Damage Likelihood Scale) para detectar y conocer el avance del glaucoma en los pacientes. Para ello, se utilizó como insumo el conjunto de imágenes REFUGE (Retinal Fundus Glaucoma Challenge), identificando la región de interés (ROI por sus siglas en inglés) mediante el algoritmo de detección de objetos YOLO (You Only Look Once).Después de esto, se procedió a realizar la medición del RDR (Rim-to-Disc Ratio) en cada grado en las imágenes segmentadas utilizando dos modelos previamente entrenados: uno para el disco y otro para la copa ocular. De esta manera, se logró asignar nuevas etiquetas a las imágenes con base la escala DDLS. Luego, se entrenó un modelo base con las etiquetas originales, el cual se comparó con tres modelos entrenados mediante aprendizaje por transferencia con las etiquetas construidas. Estos modelos utilizaron diferentes técnicas para el procesamiento de las imágenes, incluyendo la conversión de coordenadas cartesianas a polares y el recorte de las imágenes en estéreo centradas en el nervio óptico a una dimensión de 224 × 224 píxeles para contar con mayor información de la imagen. Los mejores resultados fueron obtenidos por el modelo entrenado con las imágenes convertidas a coordenadas polares. (Texto tomado de la fuente)Glaucoma is one of the most prevalent and severe diseases in the world, characterized by a gradual loss of peripheral vision that, if not treated in time, can be irreversible and lead to total vision loss. In order to facilitate early detection of this disease, various models based on deep learning and convolutional neural networks have been proposed, which allow for automated diagnosis. Despite their usefulness, these models present some limitations, such as the evaluation of neuroretinal border width only vertically and the assignment of a binary classification to denote the presence or absence of the disease, which makes it difficult to identify its stage and the progression of the disease in multiple directions. For this reason, this work presents a deep learning-based approach that uses the DDLS (Disc Damage Likelihood Scale) scale to detect and understand the progression of glaucoma in patients. For this purpose, the REFUGE (Retinal Fundus Glaucoma Challenge) image set was used as input, identifying the region of interest (ROI) using the YOLO (You Only Look Once) object detection algorithm. After this, the RDR (Rim-to-Disc Ratio) was measured at each degree in the segmented images using two previously trained models: one for the disc and one for the optic cup. In this way, new labels were assigned to the images based on the DDLS scale. Then, a baseline model was trained with the original labels, which was compared with three models trained by transfer learning with the constructed labels. These models used different techniques for image processing, including the conversion of Cartesian coordinates to polar coordinates and the cropping of stereo images centered on the optic nerve to a dimension of 224 × 224 pixels to obtain more information from the image. The best results were obtained by the model trained with images converted to polar coordinates.MaestríaMagíster en Ingeniería - Ingeniería de Sistemas y Computaciónxiv, 59 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y ComputaciónFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computaciónEnfermedades del Nervio ÓpticoOptic Nerve DiseasesGlaucomaEscala DDLSRDRYOLOAprendizaje por transferenciaRedes neuronales convolucionalesModelo de clasificaciónModelo de segmentaciónGlaucomaDDLS scaleRDRYOLOTransfer learningConvolutional neural networksClassification modelSegmentation modelModelo de simulaciónSimulation modelsModelo de aprendizaje profundo para cuantificar el daño causado por el Glaucoma en el nervio ópticoDeep learning model to quantify the damage caused by Glaucoma in the optic nerveTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TM[25] M. 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Muir, “Applications of deep learning in detection of glaucoma: A systematic review,” European Journal of Ophthalmology, 2020.InvestigadoresLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/83865/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1026591991.2023.pdf1026591991.2023.pdfTesis de Maestría en Ingeniería de Sistemas y Computaciónapplication/pdf29490025https://repositorio.unal.edu.co/bitstream/unal/83865/2/1026591991.2023.pdf75bc82991e123daaf9074988a22d8606MD52THUMBNAIL1026591991.2023.pdf.jpg1026591991.2023.pdf.jpgGenerated Thumbnailimage/jpeg4532https://repositorio.unal.edu.co/bitstream/unal/83865/3/1026591991.2023.pdf.jpg4e4f8f783c27baf41caf049e6c404a87MD53unal/83865oai:repositorio.unal.edu.co:unal/838652024-08-06 23:10:17.885Repositorio Institucional Universidad Nacional de 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