Método de identificación automática de la retinopatía diabética en imágenes del fondo de ojo, utilizando técnicas de visión artificial
| dc.contributor.advisor | Almeida Moreno, Javier Andres | |
| dc.contributor.author | Guerrero Santana, Izabella | |
| dc.date.accessioned | 2024-11-30T07:43:47Z | |
| dc.date.available | 2024-11-30T07:43:47Z | |
| dc.date.issued | 2024-11 | |
| dc.description.abstract | La retinopatía diabética (RD) es una complicación de la diabetes que surge por el daño a los pequeños vasos sanguíneos de la retina debido a niveles elevados de azúcar en sangre. Dada la creciente prevalencia de la diabetes y el riesgo de ceguera asociado, es fundamental la detección temprana de la RD. Este trabajo presenta un enfoque automatizado para la identificación de los estadios de la retinopatía diabética, utilizando descomposición de momentos de Zernike sobre imágenes de fondo de ojo. El dataset utilizado fue una combinación de cuatro conjuntos de datos: EyePACS, APTOS 2019, APTOS 2019 (Gaussian Filtered) y Messidor Diabetic Retinopathy, compilados por Abdullah et al., (2024)}. La metodología incluyó el preprocesamiento de las imágenes, extracción de características con descriptores de forma, submuestreo para equilibrar las distribuciones de clase, y clasificación mediante una máquina de vectores de soporte (SVM) y una red neuronal densa. El modelo SVM logró una sensibilidad de 0,90 y una especificidad de 0,89 en la detección de casos sin retinopatía, mientras que la identificación de estadios leves y proliferativos presentó menores valores de precisión y recall (0,60 y 0,55, respectivamente). La red neuronal densa mostró un rendimiento inferior en comparación con la SVM. El aporte principal de este trabajo radica en la implementación de una técnica híbrida que combina descriptores de forma y modelos de clasificación para mejorar la detección automática de los diferentes estadios de la retinopatía diabética. | |
| dc.description.abstractenglish | Diabetic retinopathy (DR) is a complication of diabetes that arises from damage to the small blood vessels of the retina due to elevated blood sugar levels. Given the increasing prevalence of diabetes and the associated risk of blindness, early detection of DR is critical. This paper presents an automated approach for the identification of diabetic retinopathy stages using Zernike moment decomposition on fundus images. The dataset used was a combination of four datasets: EyePACS, APTOS 2019, APTOS 2019 (Gaussian Filtered) and Messidor Diabetic Retinopathy. The methodology included image preprocessing, feature extraction with shape descriptors, subsampling to balance class distributions, and classification using a support vector machine (SVM) and a dense neural network. The SVM model achieved a sensitivity of 0.90 and a specificity of 0.89 in the detection of cases without retinopathy, while the identification of mild and proliferative stages presented lower precision and recall values (0.60 and 0.55, respectively). The dense neural network showed inferior performance compared to SVM. The main contribution of this work lies in the implementation of a hybrid technique that combines shape descriptors and classification models to improve the automatic detection of the different stages of diabetic retinopathy. | |
| dc.description.degreelevel | Pregrado | spa |
| dc.description.degreename | Bioingeniero | spa |
| dc.format.mimetype | application/pdf | |
| dc.identifier.instname | instname:Universidad El Bosque | spa |
| dc.identifier.reponame | reponame:Repositorio Institucional Universidad El Bosque | spa |
| dc.identifier.repourl | https://repositorio.unbosque.edu.co | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12495/13499 | |
| dc.language.iso | es | |
| dc.publisher.faculty | Facultad de Ingeniería | spa |
| dc.publisher.grantor | Universidad El Bosque | spa |
| dc.publisher.program | Bioingeniería | spa |
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| dc.rights | Atribución-NoComercial-CompartirIgual 4.0 Internacional | en |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | |
| dc.rights.accessrights | http:/purl.org/coar/access_right/c_abf2/ | |
| dc.rights.local | Acceso abierto | spa |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | |
| dc.subject | Clasificación automática | |
| dc.subject | Retinopatía diabética | |
| dc.subject | Momentos de Zernike | |
| dc.subject | Aprendizaje de máquina | |
| dc.subject.ddc | 610.28 | |
| dc.subject.keywords | Automatic classification | |
| dc.subject.keywords | Diabetic retinopathy | |
| dc.subject.keywords | Zernike's moments | |
| dc.subject.keywords | Machine learning | |
| dc.title | Método de identificación automática de la retinopatía diabética en imágenes del fondo de ojo, utilizando técnicas de visión artificial | |
| dc.title.translated | Method for automatic identification of diabetic retinopathy in fundus images using machine vision techniques | |
| dc.type.coar | https://purl.org/coar/resource_type/c_7a1f | |
| dc.type.coarversion | https://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| dc.type.driver | info:eu-repo/semantics/bachelorThesis | |
| dc.type.hasversion | info:eu-repo/semantics/acceptedVersion | |
| dc.type.local | Tesis/Trabajo de grado - Monografía - Pregrado | spa |