Utilización de redes neuronales y resonancias magnéticas T2 y T2-FLAIR para la detección de lesiones periventriculares de esclerosis múltiple

Este estudio tuvo como objetivo desarrollar un modelo de red neuronal convolucional para la detección de esclerosis múltiple utilizando imágenes de MRI T2 y T2-FLAIR. Se creó una base de datos a partir de 36 estudios diferentes y la base de datos de Kaggle, incluyendo tanto MRI sin esclerosis múltip...

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Autores:
Villamil Martinez, Cristian Andres
Tipo de recurso:
https://purl.org/coar/resource_type/c_7a1f
Fecha de publicación:
2023
Institución:
Universidad El Bosque
Repositorio:
Repositorio U. El Bosque
Idioma:
spa
OAI Identifier:
oai:repositorio.unbosque.edu.co:20.500.12495/14922
Acceso en línea:
https://hdl.handle.net/20.500.12495/14922
Palabra clave:
Red neuronal convolucional
Esclerosis múltiple
Resonancia magnética T2 y T2-FLAIR
Base de datos Kaggle
Optimización de hiperparámetros
510
Convolutional Neural Network
Multiple Sclerosis
T2 and T2- FLAIR MRI
Kaggle Database
Hyperparameter Optimization
Rights
License
Attribution-NoDerivatives 4.0 International
id UNBOSQUE2_3a06bbdb25cde8f6987e5735d3097cd3
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network_name_str Repositorio U. El Bosque
repository_id_str
dc.title.none.fl_str_mv Utilización de redes neuronales y resonancias magnéticas T2 y T2-FLAIR para la detección de lesiones periventriculares de esclerosis múltiple
dc.title.translated.none.fl_str_mv Use of neural networks and T2 and T2-FLAIR magnetic resonances for the detection of periventricular lesions in multiple sclerosis
title Utilización de redes neuronales y resonancias magnéticas T2 y T2-FLAIR para la detección de lesiones periventriculares de esclerosis múltiple
spellingShingle Utilización de redes neuronales y resonancias magnéticas T2 y T2-FLAIR para la detección de lesiones periventriculares de esclerosis múltiple
Red neuronal convolucional
Esclerosis múltiple
Resonancia magnética T2 y T2-FLAIR
Base de datos Kaggle
Optimización de hiperparámetros
510
Convolutional Neural Network
Multiple Sclerosis
T2 and T2- FLAIR MRI
Kaggle Database
Hyperparameter Optimization
title_short Utilización de redes neuronales y resonancias magnéticas T2 y T2-FLAIR para la detección de lesiones periventriculares de esclerosis múltiple
title_full Utilización de redes neuronales y resonancias magnéticas T2 y T2-FLAIR para la detección de lesiones periventriculares de esclerosis múltiple
title_fullStr Utilización de redes neuronales y resonancias magnéticas T2 y T2-FLAIR para la detección de lesiones periventriculares de esclerosis múltiple
title_full_unstemmed Utilización de redes neuronales y resonancias magnéticas T2 y T2-FLAIR para la detección de lesiones periventriculares de esclerosis múltiple
title_sort Utilización de redes neuronales y resonancias magnéticas T2 y T2-FLAIR para la detección de lesiones periventriculares de esclerosis múltiple
dc.creator.fl_str_mv Villamil Martinez, Cristian Andres
dc.contributor.advisor.none.fl_str_mv Duitama Leal, Alejandro
Reyes, Marco Aurelio
dc.contributor.author.none.fl_str_mv Villamil Martinez, Cristian Andres
dc.subject.none.fl_str_mv Red neuronal convolucional
Esclerosis múltiple
Resonancia magnética T2 y T2-FLAIR
Base de datos Kaggle
Optimización de hiperparámetros
topic Red neuronal convolucional
Esclerosis múltiple
Resonancia magnética T2 y T2-FLAIR
Base de datos Kaggle
Optimización de hiperparámetros
510
Convolutional Neural Network
Multiple Sclerosis
T2 and T2- FLAIR MRI
Kaggle Database
Hyperparameter Optimization
dc.subject.ddc.none.fl_str_mv 510
dc.subject.keywords.none.fl_str_mv Convolutional Neural Network
Multiple Sclerosis
T2 and T2- FLAIR MRI
Kaggle Database
Hyperparameter Optimization
description Este estudio tuvo como objetivo desarrollar un modelo de red neuronal convolucional para la detección de esclerosis múltiple utilizando imágenes de MRI T2 y T2-FLAIR. Se creó una base de datos a partir de 36 estudios diferentes y la base de datos de Kaggle, incluyendo tanto MRI sin esclerosis múltiple como MRI con esclerosis múltiple. El modelo desarrollado alcanzó una precisión de verificación del 96%. Estos hallazgos destacan el potencial de las redes neuronales convolucionales en la detección de enfermedades a través de imágenes médicas, a pesar de algunas limitaciones, como el tamaño del conjunto de datos y las restricciones en la optimización de hiperparámetros.
publishDate 2023
dc.date.issued.none.fl_str_mv 2023-11
dc.date.accessioned.none.fl_str_mv 2025-07-10T21:40:26Z
dc.date.available.none.fl_str_mv 2025-07-10T21:40:26Z
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_7a1f
dc.type.local.spa.fl_str_mv Tesis/Trabajo de grado - Monografía - Pregrado
dc.type.coar.none.fl_str_mv https://purl.org/coar/resource_type/c_7a1f
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/bachelorThesis
dc.type.coarversion.none.fl_str_mv https://purl.org/coar/version/c_ab4af688f83e57aa
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dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12495/14922
dc.identifier.instname.spa.fl_str_mv instname:Universidad El Bosque
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional Universidad El Bosque
dc.identifier.repourl.none.fl_str_mv repourl:https://repositorio.unbosque.edu.co
url https://hdl.handle.net/20.500.12495/14922
identifier_str_mv instname:Universidad El Bosque
reponame:Repositorio Institucional Universidad El Bosque
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dc.language.iso.fl_str_mv spa
language spa
dc.relation.references.none.fl_str_mv Dadhich, M., Bengio, Y., Courville, A., & Goodfellow, I. (2018). Practical Computer Vision: Extract Insightful Information From Images Using TensorFlow, Keras, and OpenCV. Packt Publishing.
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Karim, M. R. (2017). Predictive Analytics with TensorFlow.
Filippi, M., Preziosa, P., Banwell, B. L., Barkhof, F., Ciccarelli, O., De Stefano, N., Geurts, J. J. G., Paul, F., Reich, D. S., Toosy, A. T., Traboulsee, A., Wattjes, M. P., Yousry, T. A., Gass, A., Lubetzki, C., Weinshenker, B. G., & Rocca, M. A. (2019). Assessment of lesions on magnetic resonance imaging in multiple sclerosis: practical guidelines. Brain, 142(7), 1858-1875. https://doi.org/10.1093/brain/awz144
Tillema, J. M. (2023). Imaging of Central Nervous System Demyelinating Disorders. Brain, 29(xx), 292-323. https://pubmed.ncbi.nlm.nih.gov/36795881/
Banwell, B., Bennett, J. L., Marignier, R., Kim, H. J., Brilot, F., Flanagan, E. P., Ramanathan, S., Waters, P., Tenembaum, S., Graves, J. S., Chitnis, T., Brandt, A. U., Hemingway, C., Neuteboom, R., Pandit, L., Reindl, M., Saiz, A., Sato, D. K., Rostasy, K., Paul, F., Pittock, S. J., Fujihara, K., & Palace, J. (2023). Diagnosis of myelin oligodendrocyte glycoprotein antibody-associated disease: International MOGAD Panel proposed criteria. The Lancet Neurology, 22(3), 268-282. https://doi.org/10.1016/S1474-4422(22)00431-8
Hor, J. Y., Asgari, N., Nakashima, I., Broadley, S. A., Leite, M. I., Kissani, N., Jacob, A., Marignier, R., Weinshenker, B. G., Paul, F., Pittock, S. J., Palace, J., Wingerchuk, D. M., Behne, J. M., Yeaman, M. R., & Fujihara, K. (2020). Epidemiology of Neuromyelitis Optica Spectrum Disorder and Its Prevalence and Incidence Worldwide. Frontiers in Neurology, 11, 501. https://doi.org/10.3389/fneur.2020.00501
Hornik, K. (1991). Approximation capabilities of multilayer feedforward networks. Neural Networks, 4(2), 251-257. https://doi.org/10.1016/0893-6080(91)90009-T
Bakker, B., & Heskes, T. (2003). Input uncertainty and the neural network classifier: a probabilistic perspective. Neural Networks, 16(1), 31-42. https://doi.org/10.1016/S0893-6080(02)00254-3
Tousignant, A., Lemaître, P., Precup, D., Arnold, D. L., & Arbel, T. (2019). Prediction of Disease Progression in Multiple Sclerosis Patients using Deep Learning Analysis of MRI Data. In M. J. Cardoso, A. Feragen, B. Glocker, E. Konukoglu, I. Oguz, G. Unal, & T. Vercauteren (Eds.), Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning (pp. 483-492). PMLR. http://proceedings.mlr.press/v102/tousignant19a/tousignant19a.pdf
Bonacchi, R., Filippi, M., & Rocca, M. A. (2022). Role of artificial intelligence in MS clinical practice. NeuroImage: Clinical, 35, 103065. https://doi.org/10.1016/j.nicl.2022.103065
Núñez Rivera, J. (2021). Aprendizaje profunda por refuerzo para clasificación de imágenes. Neural Networks, 16(1), 31-42.
Basheer, I. A., & Hajmeer, M. (2000). Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods, 43(1), 3-31. https://doi.org/10.1016/S0167-7012(00)00201-3
Qu, L., Zhang, Y., Wang, S., Yap, P. T., & Shen, D. (2020). Synthesized 7T MRI from 3T MRI via deep learning in spatial and wavelet domains. Medical Image Analysis, 62, 101663. https://doi.org/10.1016/j.media.2020.101663
Tommasin, S., Cocozza, S., Taloni, A., Giannì, C., Petsas, N., Pontillo, G., Petracca, M., Ruggieri, S., Giglio, L., Pozzilli, C., Brunetti, A., & Pantano, P. (2021). Machine learning classifier to identify clinical and radiological features relevant to disability progression in multiple sclerosis. Journal of Neurology, 268, 101663. https://doi.org/10.1007/s00415-021-10605-7
Hartmann, M., Fenton, N., & Dobson, R. (2021). Current review and next steps for artificial intelligence in multiple sclerosis risk research. Computers in Biology and Medicine, 132, 104337. https://doi.org/10.1016/j.compbiomed.2021.104337
Hardy, N. P., Epperlein, J. P., Dalli, J., Robertson, W., Liddy, R., Aird, J. J., Mulligan, N., Neary, P. M., McEntee, G. P., Conneely, J. B., & Cahill, R. A. (2023). Real-time administration of indocyanine green in combination with computer vision and artificial intelligence for the identification and delineation of colorectal liver metastases. Surgery Open Science, 12, 48-54. https://doi.org/10.1016/j.sopen.2023.03.004
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Paragios, N., Chen, Y., & Faugeras, O. D. (2006). Handbook of mathematical models in computer vision. Springer Science & Business Media.
Yao, Y. (2016). Image Segmentation Based on Sobel Edge Detection. In Proceedings of the 2016 5th International Conference on Advanced Materials and Computer Science (pp. 141-144). Atlantis Press. https://doi.org/10.2991/icamcs-16.2016.27
Bora, D. J. (2017). A novel approach for color image edge detection using multidirectional Sobel filter on HSV color space. International Journal of Computer Science and Engineering, 5(2), 154-159.
Rocca, M. A., Anzalone, N., Storelli, L., Del Poggio, A., Cacciaguerra, L., Manfredi, A. A., Meani, A., & Filippi, M. (2021). Deep Learning on Conventional Magnetic Resonance Imaging Improves the Diagnosis of Multiple Sclerosis Mimics. Investigative Radiology, 56(4), 252-260. https://doi.org/10.1097/RLI.0000000000000735
Haykin, S. (2009). Neural networks and learning machines (3rd ed.). Pearson Education India.
Macin, G., Tasci, B., Tasci, I., Faust, O., Barua, P. D., Dogan, S., Tuncer, T., Tan, R.-S., & Acharya, U. R. (2022). An Accurate Multiple Sclerosis Detection Model Based on Exemplar Multiple Parameters Local Phase Quantization: ExMPLPQ. Applied Sciences, 12(10), 4920. https://doi.org/10.3390/app12104920
Yuan, X., Hu, K., & Chen, S. (2020). Realtime CNN-based Keypoint Detector with Sobel Filter and CNN-based Descriptor Trained with Keypoint Candidates. arXiv. https://doi.org/10.48550/arXiv.2011.02119
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Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2022). A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects. IEEE Transactions on Neural Networks and Learning Systems, 33(12), 6999-7019. https://doi.org/10.1109/TNNLS.2021.3084827
Szeliski, R. (2010). Computer Vision: Algorithms and Applications. Springer Science & Business Media.
Rasamoelina, A. D., Adjailia, F., & Sinčák, P. (2020). A Review of Activation Function for Artificial Neural Network. In 2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI) (pp. 281-286). IEEE. https://doi.org/10.1109/SAMI48414.2020.9108717
Huang, J. J., & Wanda, P. (2020). RunPool: A Dynamic Pooling Layer for Convolution Neural Network. International Journal of Computational Intelligence Systems, 13(1), 66-76.
Polman, C. H., Reingold, S. C., Banwell, B., Clanet, M., Cohen, J. A., Filippi, M., ... & Kappos, L. (2011). Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Annals of Neurology, 69(2), 292-302. https://doi.org/10.1002/ana.22366
Thompson, A. J., Banwell, B. L., Barkhof, F., Carroll, W. M., Coetzee, T., Comi, G., ... & Kappos, L. (2018). Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. The Lancet Neurology, 17(2), 162-173. https://doi.org/10.1016/S1474-4422(17)30470-2
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Zivadinov, R., & Leist, T. (2018). Clinical-magnetic resonance imaging correlations in multiple sclerosis. Journal of Neuroimaging, 28(3), 267-275. https://doi.org/10.1111/jon.12511
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spelling Duitama Leal, AlejandroReyes, Marco AurelioVillamil Martinez, Cristian Andres2025-07-10T21:40:26Z2025-07-10T21:40:26Z2023-11https://hdl.handle.net/20.500.12495/14922instname:Universidad El Bosquereponame:Repositorio Institucional Universidad El Bosquerepourl:https://repositorio.unbosque.edu.coEste estudio tuvo como objetivo desarrollar un modelo de red neuronal convolucional para la detección de esclerosis múltiple utilizando imágenes de MRI T2 y T2-FLAIR. Se creó una base de datos a partir de 36 estudios diferentes y la base de datos de Kaggle, incluyendo tanto MRI sin esclerosis múltiple como MRI con esclerosis múltiple. El modelo desarrollado alcanzó una precisión de verificación del 96%. Estos hallazgos destacan el potencial de las redes neuronales convolucionales en la detección de enfermedades a través de imágenes médicas, a pesar de algunas limitaciones, como el tamaño del conjunto de datos y las restricciones en la optimización de hiperparámetros.MatemáticoPregradoThis study aimed at developing a convolutional neural network model for the detection of multiple sclerosis using T2 and T2-FLAIR MRI images. A database was created from 36 different studies and the Kaggle database, including both MRI without MS and MRI with MS. The developed model achieved a verification accuracy of 96%. These findings highlight the potential of convolutional neural networks in disease detection through medical imaging, despite some limitations such as dataset size and hyperparameter optimization constraints.application/pdfAttribution-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nd/4.0/Acceso abiertohttps://purl.org/coar/access_right/c_abf2http://purl.org/coar/access_right/c_abf2Red neuronal convolucionalEsclerosis múltipleResonancia magnética T2 y T2-FLAIRBase de datos KaggleOptimización de hiperparámetros510Convolutional Neural NetworkMultiple SclerosisT2 and T2- FLAIR MRIKaggle DatabaseHyperparameter OptimizationUtilización de redes neuronales y resonancias magnéticas T2 y T2-FLAIR para la detección de lesiones periventriculares de esclerosis múltipleUse of neural networks and T2 and T2-FLAIR magnetic resonances for the detection of periventricular lesions in multiple sclerosisMatemáticasUniversidad El BosqueFacultad de CienciasTesis/Trabajo de grado - Monografía - Pregradohttps://purl.org/coar/resource_type/c_7a1fhttp://purl.org/coar/resource_type/c_7a1finfo:eu-repo/semantics/bachelorThesishttps://purl.org/coar/version/c_ab4af688f83e57aaDadhich, M., Bengio, Y., Courville, A., & Goodfellow, I. (2018). Practical Computer Vision: Extract Insightful Information From Images Using TensorFlow, Keras, and OpenCV. Packt Publishing.Artasanchez, A., & Joshi, P. (2020). Artificial Intelligence with Python: Your Complete Guide to Building Intelligent Apps Using Python 3.X and TensorFlow 2 (2nd ed.). Packt Publishing. https://books.google.com.co/books?id=Urc9zQEACAAJKarim, M. R. (2017). Predictive Analytics with TensorFlow.Filippi, M., Preziosa, P., Banwell, B. L., Barkhof, F., Ciccarelli, O., De Stefano, N., Geurts, J. J. G., Paul, F., Reich, D. S., Toosy, A. T., Traboulsee, A., Wattjes, M. P., Yousry, T. A., Gass, A., Lubetzki, C., Weinshenker, B. G., & Rocca, M. A. (2019). Assessment of lesions on magnetic resonance imaging in multiple sclerosis: practical guidelines. Brain, 142(7), 1858-1875. https://doi.org/10.1093/brain/awz144Tillema, J. M. (2023). Imaging of Central Nervous System Demyelinating Disorders. Brain, 29(xx), 292-323. https://pubmed.ncbi.nlm.nih.gov/36795881/Banwell, B., Bennett, J. L., Marignier, R., Kim, H. J., Brilot, F., Flanagan, E. P., Ramanathan, S., Waters, P., Tenembaum, S., Graves, J. S., Chitnis, T., Brandt, A. U., Hemingway, C., Neuteboom, R., Pandit, L., Reindl, M., Saiz, A., Sato, D. K., Rostasy, K., Paul, F., Pittock, S. J., Fujihara, K., & Palace, J. (2023). Diagnosis of myelin oligodendrocyte glycoprotein antibody-associated disease: International MOGAD Panel proposed criteria. The Lancet Neurology, 22(3), 268-282. https://doi.org/10.1016/S1474-4422(22)00431-8Hor, J. Y., Asgari, N., Nakashima, I., Broadley, S. A., Leite, M. I., Kissani, N., Jacob, A., Marignier, R., Weinshenker, B. G., Paul, F., Pittock, S. J., Palace, J., Wingerchuk, D. M., Behne, J. M., Yeaman, M. R., & Fujihara, K. (2020). Epidemiology of Neuromyelitis Optica Spectrum Disorder and Its Prevalence and Incidence Worldwide. Frontiers in Neurology, 11, 501. https://doi.org/10.3389/fneur.2020.00501Hornik, K. (1991). Approximation capabilities of multilayer feedforward networks. Neural Networks, 4(2), 251-257. https://doi.org/10.1016/0893-6080(91)90009-TBakker, B., & Heskes, T. (2003). Input uncertainty and the neural network classifier: a probabilistic perspective. Neural Networks, 16(1), 31-42. https://doi.org/10.1016/S0893-6080(02)00254-3Tousignant, A., Lemaître, P., Precup, D., Arnold, D. L., & Arbel, T. (2019). Prediction of Disease Progression in Multiple Sclerosis Patients using Deep Learning Analysis of MRI Data. In M. J. Cardoso, A. Feragen, B. Glocker, E. Konukoglu, I. Oguz, G. Unal, & T. Vercauteren (Eds.), Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning (pp. 483-492). PMLR. http://proceedings.mlr.press/v102/tousignant19a/tousignant19a.pdfBonacchi, R., Filippi, M., & Rocca, M. A. (2022). Role of artificial intelligence in MS clinical practice. NeuroImage: Clinical, 35, 103065. https://doi.org/10.1016/j.nicl.2022.103065Núñez Rivera, J. (2021). Aprendizaje profunda por refuerzo para clasificación de imágenes. Neural Networks, 16(1), 31-42.Basheer, I. A., & Hajmeer, M. (2000). Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods, 43(1), 3-31. https://doi.org/10.1016/S0167-7012(00)00201-3Qu, L., Zhang, Y., Wang, S., Yap, P. T., & Shen, D. (2020). Synthesized 7T MRI from 3T MRI via deep learning in spatial and wavelet domains. Medical Image Analysis, 62, 101663. https://doi.org/10.1016/j.media.2020.101663Tommasin, S., Cocozza, S., Taloni, A., Giannì, C., Petsas, N., Pontillo, G., Petracca, M., Ruggieri, S., Giglio, L., Pozzilli, C., Brunetti, A., & Pantano, P. (2021). Machine learning classifier to identify clinical and radiological features relevant to disability progression in multiple sclerosis. 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