Regularización de redes neuronales artificiales para la clasificación de imágenes de retinopatía diabética

Ilustraciones

Autores:: Ramírez Sánchez, Juan David

Tipo de recurso:

Fecha de publicación:: 2022

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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repository_id_str
dc.title.spa.fl_str_mv	Regularización de redes neuronales artificiales para la clasificación de imágenes de retinopatía diabética
dc.title.translated.eng.fl_str_mv	Regularization of artificial neural networks for image classification of diabetic retinopathy
title	Regularización de redes neuronales artificiales para la clasificación de imágenes de retinopatía diabética
spellingShingle	Regularización de redes neuronales artificiales para la clasificación de imágenes de retinopatía diabética 610 - Medicina y salud 000 - Ciencias de la computación, información y obras generales::003 - Sistemas Redes neuronales (Computadoers) Aprendizaje automático (Inteligencia artificial) Procesamiento de imágenes Retinopatía Diabética Redes Neuronales Convolucionales Sobreajuste Técnicas de regularización Diabetic retinopathy Convolutional Neural Networks Overfitting Transfer learning Regularization techniques Tensorflow Keras
title_short	Regularización de redes neuronales artificiales para la clasificación de imágenes de retinopatía diabética
title_full	Regularización de redes neuronales artificiales para la clasificación de imágenes de retinopatía diabética
title_fullStr	Regularización de redes neuronales artificiales para la clasificación de imágenes de retinopatía diabética
title_full_unstemmed	Regularización de redes neuronales artificiales para la clasificación de imágenes de retinopatía diabética
title_sort	Regularización de redes neuronales artificiales para la clasificación de imágenes de retinopatía diabética
dc.creator.fl_str_mv	Ramírez Sánchez, Juan David
dc.contributor.advisor.none.fl_str_mv	Villa Garzón, Fernán Alonso
dc.contributor.author.none.fl_str_mv	Ramírez Sánchez, Juan David
dc.subject.ddc.spa.fl_str_mv	610 - Medicina y salud 000 - Ciencias de la computación, información y obras generales::003 - Sistemas
topic	610 - Medicina y salud 000 - Ciencias de la computación, información y obras generales::003 - Sistemas Redes neuronales (Computadoers) Aprendizaje automático (Inteligencia artificial) Procesamiento de imágenes Retinopatía Diabética Redes Neuronales Convolucionales Sobreajuste Técnicas de regularización Diabetic retinopathy Convolutional Neural Networks Overfitting Transfer learning Regularization techniques Tensorflow Keras
dc.subject.lemb.none.fl_str_mv	Redes neuronales (Computadoers) Aprendizaje automático (Inteligencia artificial) Procesamiento de imágenes
dc.subject.proposal.spa.fl_str_mv	Retinopatía Diabética Redes Neuronales Convolucionales Sobreajuste Técnicas de regularización
dc.subject.proposal.eng.fl_str_mv	Diabetic retinopathy Convolutional Neural Networks Overfitting Transfer learning Regularization techniques Tensorflow Keras
description	Ilustraciones
publishDate	2022
dc.date.accessioned.none.fl_str_mv	2022-08-17T21:54:35Z
dc.date.available.none.fl_str_mv	2022-08-17T21:54:35Z
dc.date.issued.none.fl_str_mv	2022-02-28
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
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dc.type.content.spa.fl_str_mv	Text
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status_str	acceptedVersion
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dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
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identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	Abdelmaksoud, E., El-Sappagh, S., Barakat, S., Abuhmed, T., & Elmogy, M. (2021). Automatic Diabetic Retinopathy Grading System Based on Detecting Multiple Retinal Lesions. IEEE Access, 9, 15939-15960. https://doi.org/10.1109/ACCESS.2021.3052870 Agustin, T., & Sunyoto, A. (2020). Optimization Convolutional Neural Network for Classification Diabetic Retinopathy Severity. 2020 3rd International Conference on Information and Communications Technology (ICOIACT), 66-71. https://doi.org/10.1109/ICOIACT50329.2020.9332087 Alyoubi, W. L., Shalash, W. M., & Abulkhair, M. F. (2020). Diabetic retinopathy detection through deep learning techniques: A review. Informatics in Medicine Unlocked, 20, 100377. https://doi.org/10.1016/j.imu.2020.100377 APTOS 2019 Blindness Detection. (s. f.). Recuperado 28 de febrero de 2022, de https://kaggle.com/c/aptos2019-blindness-detection Barhate, N., Bhave, S., Bhise, R., Sutar, R. G., & Karia, D. C. (2020). Reducing Overfitting in Diabetic Retinopathy Detection using Transfer Learning. 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), 298-301. https://doi.org/10.1109/ICCCA49541.2020.9250772 Benzamin, A., & Chakraborty, C. (2018). Detection of Hard Exudates in Retinal Fundus Images Using Deep Learning. 2018 Joint 7th International Conference on Informatics, Electronics Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision Pattern Recognition (icIVPR), 465-469. https://doi.org/10.1109/ICIEV.2018.8641016 Chetoui, M., Akhloufi, M. A., & Kardouchi, M. (2018). Diabetic Retinopathy Detection Using Machine Learning and Texture Features. 2018 IEEE Canadian Conference on Electrical Computer Engineering (CCECE), 1-4. https://doi.org/10.1109/CCECE.2018.8447809 Chudzik, P., Majumdar, S., Calivá, F., Al-Diri, B., & Hunter, A. (2018). Microaneurysm detection using fully convolutional neural networks. Computer Methods and Programs in Biomedicine, 158, 185-192. https://doi.org/10.1016/j.cmpb.2018.02.016 Das, S., Kharbanda, K., M, S., Raman, R., & D, E. D. (2021). Deep learning architecture based on segmented fundus image features for classification of diabetic retinopathy. Biomedical Signal Processing and Control, 68, 102600. https://doi.org/10.1016/j.bspc.2021.102600 Diabetic Retinopathy 224x224 Gaussian Filtered. (s. f.). Recuperado 28 de febrero de 2022, de https://kaggle.com/sovitrath/diabetic-retinopathy-224x224-gaussian-filtered Fong, D. S., Aiello, L., Gardner, T. W., King, G. L., Blankenship, G., Cavallerano, J. D., Ferris, F. L., & Klein, R. (2004). Retinopathy in Diabetes. Diabetes Care, 27(suppl 1), s84-s87. https://doi.org/10.2337/diacare.27.2007.S84 Galdran, A., Dolz, J., Chakor, H., Lombaert, H., & Ayed, I. B. (2020). Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images. arXiv:2010.00291 [cs]. http://arxiv.org/abs/2010.00291 Hemanth, D. J., Deperlioglu, O., & Kose, U. (2020). An enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network. Neural Computing and Applications, 32(3), 707-721. https://doi.org/10.1007/s00521-018-03974-0 Keras: The Python deep learning API. (s. f.). Recuperado 14 de febrero de 2022, de https://keras.io/ Kumar, S., Adarsh, A., Kumar, B., & Singh, A. K. (2020). An automated early diabetic retinopathy detection through improved blood vessel and optic disc segmentation. Optics & Laser Technology, 121, 105815. https://doi.org/10.1016/j.optlastec.2019.105815 Lahmiri, S. (2020). Hybrid deep learning convolutional neural networks and optimal nonlinear support vector machine to detect presence of hemorrhage in retina. Biomedical Signal Processing and Control, 60, 101978. https://doi.org/10.1016/j.bspc.2020.101978 Lois, N., Cook, J. A., Wang, A., Aldington, S., Mistry, H., Maredza, M., McAuley, D., Aslam, T., Bailey, C., Chong, V., Ganchi, F., Scanlon, P., Sivaprasad, S., Steel, D. H., Styles, C., Azuara-Blanco, A., Prior, L., Waugh, N., Saad, A., … Chong, V. (2021). Evaluation of a New Model of Care for People with Complications of Diabetic Retinopathy: The EMERALD Study. Ophthalmology, 128(4), 561-573. https://doi.org/10.1016/j.ophtha.2020.10.030 Lokuarachchi, D., Gunarathna, K., Muthumal, L., & Gamage, T. (2019). Automated Detection of Exudates in Retinal Images. 2019 IEEE 15th International Colloquium on Signal Processing Its Applications (CSPA), 43-47. https://doi.org/10.1109/CSPA.2019.8696052 Lyu, K., Li, Y., & Zhang, Z. (2020). Attention-Aware Multi-Task Convolutional Neural Networks. IEEE Transactions on Image Processing, 29, 1867-1878. https://doi.org/10.1109/TIP.2019.2944522 Majumder, S., & Kehtarnavaz, N. (2021). Multitasking Deep Learning Model for Detection of Five Stages of Diabetic Retinopathy. arXiv:2103.04207 [cs, eess]. http://arxiv.org/abs/2103.04207 Mathews, M. R., & Anzar, S. M. (s. f.). A comprehensive review on automated systems for severity grading of diabetic retinopathy and macular edema. International Journal of Imaging Systems and Technology, n/a(n/a). https://doi.org/10.1002/ima.22574 Pires, R., Avila, S., Wainer, J., Valle, E., Abramoff, M. D., & Rocha, A. (2019). A data-driven approach to referable diabetic retinopathy detection. Artificial Intelligence in Medicine, 96, 93-106. https://doi.org/10.1016/j.artmed.2019.03.009 Pratt, H., Coenen, F., Broadbent, D. M., Harding, S. P., & Zheng, Y. (2016). Convolutional Neural Networks for Diabetic Retinopathy. Procedia Computer Science, 90, 200-205. https://doi.org/10.1016/j.procs.2016.07.014 Rao, M., Zhu, M., & Wang, T. (2020). Conversion and Implementation of State-of-the-Art Deep Learning Algorithms for the Classification of Diabetic Retinopathy. arXiv:2010.11692 [cs]. http://arxiv.org/abs/2010.11692 Reed, R., & MarksII, R. J. (1999). Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks. MIT Press Shah, P., Mishra, D. K., Shanmugam, M. P., Doshi, B., Jayaraj, H., & Ramanjulu, R. (2020). Validation of Deep Convolutional Neural Network-based algorithm for detection of diabetic retinopathy – Artificial intelligence versus clinician for screening. Indian Journal of Ophthalmology, 68(2), 398-405. https://doi.org/10.4103/ijo.IJO_966_19 Sri, R. M., Jyothirmai, J., & Swetha, D. (s. f.). Analysis of Retinal Blood Vessel Segmentation in different types of Diabetic Retinopathy. 8(2), 4. Zhang, W., Zhong, J., Yang, S., Gao, Z., Hu, J., Chen, Y., & Yi, Z. (2019). Automated identification and grading system of diabetic retinopathy using deep neural networks. Knowledge-Based Systems, 175, 12-25. https://doi.org/10.1016/j.knosys.2019.03.016 Zhu, J., Zhang, E., & Rio-Tsonis, K. D. (2012). Eye Anatomy. En ELS. American Cancer Society. https://doi.org/10.1002/9780470015902.a0000108.pub2 jdramirezs. (2022). Regularizaci-n_de_redes_neuronales_artificiales_para_la_clasificacion_de_imagenes_de_retinopatia [Jupyter Notebook]. https://github.com/jdramirezs/Regularizaci-n_de_redes_neuronales_artificiales_para_la_clasificacion_de_imagenes_de_retinopatia (Original work published 2022)
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dc.rights.license.spa.fl_str_mv	Reconocimiento 4.0 Internacional
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dc.format.extent.spa.fl_str_mv	xviii, 95 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Medellín - Minas - Maestría en Ingeniería - Analítica
dc.publisher.department.spa.fl_str_mv	Departamento de la Computación y la Decisión
dc.publisher.faculty.spa.fl_str_mv	Facultad de Minas
dc.publisher.place.spa.fl_str_mv	Medellín
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Medellín
institution	Universidad Nacional de Colombia
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spelling	Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Villa Garzón, Fernán Alonso9c83ea56495b8f17a79c27fd0001bb81600Ramírez Sánchez, Juan David61657d2e64822ec0a064f9ae1b9f36952022-08-17T21:54:35Z2022-08-17T21:54:35Z2022-02-28https://repositorio.unal.edu.co/handle/unal/81945Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/IlustracionesLa retinopatía diabética (RD) es una patología retiniana causada por la diabetes, y es una de las principales causas de ceguera en todo el mundo; su detección temprana es primordial con el fin de prevenir su avance en el paciente. Existen diversos métodos para el diagnóstico temprano, entre estos, se ha evidenciado que las redes neuronales convolucionales (CNN – Convolucional Neural Networks) son adecuadas para el análisis de este fenómeno, contribuyendo con el diagnóstico temprano de esta enfermedad. Además, se han empleado técnicas de aprendizaje profundo (DL – Deep Learning), los modelos planteados en la literatura se centran en las etapas de preprocesamiento, extracción y selección de características de la imagen; sin embargo, estos modelos pueden adolecer de sobreajuste (Overfitting) y no se ha considerado el uso de técnicas de regularización para controlarlo. Entonces, en el presente trabajo, se ha propuesto desde un punto de vista conceptual y experimental, la selección de cinco técnicas de regularización sobre cinco modelos de aprendizaje profundo preentrenados y mediante el análisis de métricas (precisión, Recall, F1 score) se determina una técnica de regularización de redes neuronales artificiales que mejora la capacidad de generalización para la clasificación de imágenes de retinopatía diabética. (Texto tomado de la fuente)Diabetic retinopathy (DR) is a retinal pathology caused by diabetes, and is one of the main causes of blindness worldwide; Its early detection is essential in order to prevent its progression in the patient. There are various methods for early diagnosis, among these, it has been shown that convolutional neural networks (CNN) are suitable for the analysis of this phenomenon, contributing to the early diagnosis of this disease. In addition, deep learning techniques (DL – Deep Learning) have been used, the models proposed in the literature focus on the stages of preprocessing, extraction and selection of image features; however, these models may suffer from overfitting and the use of regularization techniques to control it has not been considered. So, in the present work, it has been proposed from a conceptual and experimental point of view, the selection of five regularization techniques on five pretrained deep learning models and through the analysis of metrics (precision, Recall, F1 score) a Artificial neural network regularization technique that improves the generalization capacity for the classification of diabetic retinopathy images. it is an abbreviated presentation. A maximum length of 250 words should be used. It is recommended that this summary be analytical, that is, that it be complete, with quantitative and qualitative information, generally including the following aspects: objectives, design, place and circumstances, patients (or objective of the study), intervention, measurements and main results, and conclusions. At the end of the summary, keywords taken from the text should be used, which allow the retrieval of informationMaestríaMagíster en Ingeniería - AnalíticaInteligencia ArtificialÁrea Curricular de Ingeniería de Sistemas e Informáticaxviii, 95 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería - AnalíticaDepartamento de la Computación y la DecisiónFacultad de MinasMedellínUniversidad Nacional de Colombia - Sede Medellín610 - Medicina y salud000 - Ciencias de la computación, información y obras generales::003 - SistemasRedes neuronales (Computadoers)Aprendizaje automático (Inteligencia artificial)Procesamiento de imágenesRetinopatía DiabéticaRedes Neuronales ConvolucionalesSobreajusteTécnicas de regularizaciónDiabetic retinopathyConvolutional Neural NetworksOverfittingTransfer learningRegularization techniquesTensorflowKerasRegularización de redes neuronales artificiales para la clasificación de imágenes de retinopatía diabéticaRegularization of artificial neural networks for image classification of diabetic retinopathyTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAbdelmaksoud, E., El-Sappagh, S., Barakat, S., Abuhmed, T., & Elmogy, M. (2021). Automatic Diabetic Retinopathy Grading System Based on Detecting Multiple Retinal Lesions. IEEE Access, 9, 15939-15960. https://doi.org/10.1109/ACCESS.2021.3052870Agustin, T., & Sunyoto, A. (2020). Optimization Convolutional Neural Network for Classification Diabetic Retinopathy Severity. 2020 3rd International Conference on Information and Communications Technology (ICOIACT), 66-71. https://doi.org/10.1109/ICOIACT50329.2020.9332087Alyoubi, W. L., Shalash, W. M., & Abulkhair, M. F. (2020). Diabetic retinopathy detection through deep learning techniques: A review. Informatics in Medicine Unlocked, 20, 100377. https://doi.org/10.1016/j.imu.2020.100377APTOS 2019 Blindness Detection. (s. f.). Recuperado 28 de febrero de 2022, de https://kaggle.com/c/aptos2019-blindness-detectionBarhate, N., Bhave, S., Bhise, R., Sutar, R. G., & Karia, D. C. (2020). Reducing Overfitting in Diabetic Retinopathy Detection using Transfer Learning. 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), 298-301. https://doi.org/10.1109/ICCCA49541.2020.9250772Benzamin, A., & Chakraborty, C. (2018). Detection of Hard Exudates in Retinal Fundus Images Using Deep Learning. 2018 Joint 7th International Conference on Informatics, Electronics Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision Pattern Recognition (icIVPR), 465-469. https://doi.org/10.1109/ICIEV.2018.8641016Chetoui, M., Akhloufi, M. A., & Kardouchi, M. (2018). Diabetic Retinopathy Detection Using Machine Learning and Texture Features. 2018 IEEE Canadian Conference on Electrical Computer Engineering (CCECE), 1-4. https://doi.org/10.1109/CCECE.2018.8447809Chudzik, P., Majumdar, S., Calivá, F., Al-Diri, B., & Hunter, A. (2018). Microaneurysm detection using fully convolutional neural networks. Computer Methods and Programs in Biomedicine, 158, 185-192. https://doi.org/10.1016/j.cmpb.2018.02.016Das, S., Kharbanda, K., M, S., Raman, R., & D, E. D. (2021). Deep learning architecture based on segmented fundus image features for classification of diabetic retinopathy. Biomedical Signal Processing and Control, 68, 102600. https://doi.org/10.1016/j.bspc.2021.102600Diabetic Retinopathy 224x224 Gaussian Filtered. (s. f.). Recuperado 28 de febrero de 2022, de https://kaggle.com/sovitrath/diabetic-retinopathy-224x224-gaussian-filteredFong, D. S., Aiello, L., Gardner, T. W., King, G. L., Blankenship, G., Cavallerano, J. D., Ferris, F. L., & Klein, R. (2004). Retinopathy in Diabetes. Diabetes Care, 27(suppl 1), s84-s87. https://doi.org/10.2337/diacare.27.2007.S84Galdran, A., Dolz, J., Chakor, H., Lombaert, H., & Ayed, I. B. (2020). Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images. arXiv:2010.00291 [cs]. http://arxiv.org/abs/2010.00291Hemanth, D. J., Deperlioglu, O., & Kose, U. (2020). An enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network. Neural Computing and Applications, 32(3), 707-721. https://doi.org/10.1007/s00521-018-03974-0Keras: The Python deep learning API. (s. f.). Recuperado 14 de febrero de 2022, de https://keras.io/Kumar, S., Adarsh, A., Kumar, B., & Singh, A. K. (2020). An automated early diabetic retinopathy detection through improved blood vessel and optic disc segmentation. Optics & Laser Technology, 121, 105815. https://doi.org/10.1016/j.optlastec.2019.105815Lahmiri, S. (2020). Hybrid deep learning convolutional neural networks and optimal nonlinear support vector machine to detect presence of hemorrhage in retina. Biomedical Signal Processing and Control, 60, 101978. https://doi.org/10.1016/j.bspc.2020.101978Lois, N., Cook, J. A., Wang, A., Aldington, S., Mistry, H., Maredza, M., McAuley, D., Aslam, T., Bailey, C., Chong, V., Ganchi, F., Scanlon, P., Sivaprasad, S., Steel, D. H., Styles, C., Azuara-Blanco, A., Prior, L., Waugh, N., Saad, A., … Chong, V. (2021). Evaluation of a New Model of Care for People with Complications of Diabetic Retinopathy: The EMERALD Study. Ophthalmology, 128(4), 561-573. https://doi.org/10.1016/j.ophtha.2020.10.030Lokuarachchi, D., Gunarathna, K., Muthumal, L., & Gamage, T. (2019). Automated Detection of Exudates in Retinal Images. 2019 IEEE 15th International Colloquium on Signal Processing Its Applications (CSPA), 43-47. https://doi.org/10.1109/CSPA.2019.8696052Lyu, K., Li, Y., & Zhang, Z. (2020). Attention-Aware Multi-Task Convolutional Neural Networks. IEEE Transactions on Image Processing, 29, 1867-1878. https://doi.org/10.1109/TIP.2019.2944522Majumder, S., & Kehtarnavaz, N. (2021). Multitasking Deep Learning Model for Detection of Five Stages of Diabetic Retinopathy. arXiv:2103.04207 [cs, eess]. http://arxiv.org/abs/2103.04207Mathews, M. R., & Anzar, S. M. (s. f.). A comprehensive review on automated systems for severity grading of diabetic retinopathy and macular edema. International Journal of Imaging Systems and Technology, n/a(n/a). https://doi.org/10.1002/ima.22574Pires, R., Avila, S., Wainer, J., Valle, E., Abramoff, M. D., & Rocha, A. (2019). A data-driven approach to referable diabetic retinopathy detection. Artificial Intelligence in Medicine, 96, 93-106. https://doi.org/10.1016/j.artmed.2019.03.009Pratt, H., Coenen, F., Broadbent, D. M., Harding, S. P., & Zheng, Y. (2016). Convolutional Neural Networks for Diabetic Retinopathy. Procedia Computer Science, 90, 200-205. https://doi.org/10.1016/j.procs.2016.07.014Rao, M., Zhu, M., & Wang, T. (2020). Conversion and Implementation of State-of-the-Art Deep Learning Algorithms for the Classification of Diabetic Retinopathy. arXiv:2010.11692 [cs]. http://arxiv.org/abs/2010.11692Reed, R., & MarksII, R. J. (1999). Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks. MIT PressShah, P., Mishra, D. K., Shanmugam, M. P., Doshi, B., Jayaraj, H., & Ramanjulu, R. (2020). Validation of Deep Convolutional Neural Network-based algorithm for detection of diabetic retinopathy – Artificial intelligence versus clinician for screening. Indian Journal of Ophthalmology, 68(2), 398-405. https://doi.org/10.4103/ijo.IJO_966_19Sri, R. M., Jyothirmai, J., & Swetha, D. (s. f.). Analysis of Retinal Blood Vessel Segmentation in different types of Diabetic Retinopathy. 8(2), 4.Zhang, W., Zhong, J., Yang, S., Gao, Z., Hu, J., Chen, Y., & Yi, Z. (2019). Automated identification and grading system of diabetic retinopathy using deep neural networks. Knowledge-Based Systems, 175, 12-25. https://doi.org/10.1016/j.knosys.2019.03.016Zhu, J., Zhang, E., & Rio-Tsonis, K. D. (2012). Eye Anatomy. En ELS. American Cancer Society. https://doi.org/10.1002/9780470015902.a0000108.pub2jdramirezs. (2022). Regularizaci-n_de_redes_neuronales_artificiales_para_la_clasificacion_de_imagenes_de_retinopatia [Jupyter Notebook]. https://github.com/jdramirezs/Regularizaci-n_de_redes_neuronales_artificiales_para_la_clasificacion_de_imagenes_de_retinopatia (Original work published 2022)EstudiantesORIGINAL1128430332.2021.pdf1128430332.2021.pdfTesis Maestría en Ingeniería - Analíticaapplication/pdf1795130https://repositorio.unal.edu.co/bitstream/unal/81945/3/1128430332.2021.pdf7cfa141aada8a3c4a29458d9ef866e9aMD53LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81945/4/license.txt8153f7789df02f0a4c9e079953658ab2MD54THUMBNAIL1128430332.2021.pdf.jpg1128430332.2021.pdf.jpgGenerated Thumbnailimage/jpeg5112https://repositorio.unal.edu.co/bitstream/unal/81945/5/1128430332.2021.pdf.jpg168b762158ec342b45266c4f925c19ddMD55unal/81945oai:repositorio.unal.edu.co:unal/819452023-10-06 17:20:39.901Repositorio Institucional Universidad Nacional de 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EVESURBIFBPUiBMQSBTRUNSRVRBUsONQSBHRU5FUkFMLiAqTEEgVEVTSVMgQSBQVUJMSUNBUiBERUJFIFNFUiBMQSBWRVJTScOTTiBGSU5BTCBBUFJPQkFEQS4gCgpBbCBoYWNlciBjbGljIGVuIGVsIHNpZ3VpZW50ZSBib3TDs24sIHVzdGVkIGluZGljYSBxdWUgZXN0w6EgZGUgYWN1ZXJkbyBjb24gZXN0b3MgdMOpcm1pbm9zLiBTaSB0aWVuZSBhbGd1bmEgZHVkYSBzb2JyZSBsYSBsaWNlbmNpYSwgcG9yIGZhdm9yLCBjb250YWN0ZSBjb24gZWwgYWRtaW5pc3RyYWRvciBkZWwgc2lzdGVtYS4KClVOSVZFUlNJREFEIE5BQ0lPTkFMIERFIENPTE9NQklBIC0gw5psdGltYSBtb2RpZmljYWNpw7NuIDE5LzEwLzIwMjEK

Regularización de redes neuronales artificiales para la clasificación de imágenes de retinopatía diabética

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