Clasificador de Máquinas de Vectores de Soporte para el Apoyo en la Detección del Grado I y II de Osteoartritis de Rodilla Según Kellgren- Lawrence Mediante Imágenes de Rayos X.

Propia

Autores:
Sanchez Vasquez, Maria Jose
Bastos Claros, Carlos Alberto
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2020
Institución:
Universidad Antonio Nariño
Repositorio:
Repositorio UAN
Idioma:
spa
OAI Identifier:
oai:repositorio.uan.edu.co:123456789/3159
Acceso en línea:
http://repositorio.uan.edu.co/handle/123456789/3159
Palabra clave:
Osteoartritis
SVM
Aprendizaje de máquina
Características Kellgren-Lawrence
Rayos X
Osteoarthritis
SVM
Machine Learning
Kellgren-Lawrence Features
X- Ray
Rights
openAccess
License
Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0)
id UAntonioN2_f991cc13b0c8691108fa336f20e5c201
oai_identifier_str oai:repositorio.uan.edu.co:123456789/3159
network_acronym_str UAntonioN2
network_name_str Repositorio UAN
repository_id_str
dc.title.es_ES.fl_str_mv Clasificador de Máquinas de Vectores de Soporte para el Apoyo en la Detección del Grado I y II de Osteoartritis de Rodilla Según Kellgren- Lawrence Mediante Imágenes de Rayos X.
title Clasificador de Máquinas de Vectores de Soporte para el Apoyo en la Detección del Grado I y II de Osteoartritis de Rodilla Según Kellgren- Lawrence Mediante Imágenes de Rayos X.
spellingShingle Clasificador de Máquinas de Vectores de Soporte para el Apoyo en la Detección del Grado I y II de Osteoartritis de Rodilla Según Kellgren- Lawrence Mediante Imágenes de Rayos X.
Osteoartritis
SVM
Aprendizaje de máquina
Características Kellgren-Lawrence
Rayos X
Osteoarthritis
SVM
Machine Learning
Kellgren-Lawrence Features
X- Ray
title_short Clasificador de Máquinas de Vectores de Soporte para el Apoyo en la Detección del Grado I y II de Osteoartritis de Rodilla Según Kellgren- Lawrence Mediante Imágenes de Rayos X.
title_full Clasificador de Máquinas de Vectores de Soporte para el Apoyo en la Detección del Grado I y II de Osteoartritis de Rodilla Según Kellgren- Lawrence Mediante Imágenes de Rayos X.
title_fullStr Clasificador de Máquinas de Vectores de Soporte para el Apoyo en la Detección del Grado I y II de Osteoartritis de Rodilla Según Kellgren- Lawrence Mediante Imágenes de Rayos X.
title_full_unstemmed Clasificador de Máquinas de Vectores de Soporte para el Apoyo en la Detección del Grado I y II de Osteoartritis de Rodilla Según Kellgren- Lawrence Mediante Imágenes de Rayos X.
title_sort Clasificador de Máquinas de Vectores de Soporte para el Apoyo en la Detección del Grado I y II de Osteoartritis de Rodilla Según Kellgren- Lawrence Mediante Imágenes de Rayos X.
dc.creator.fl_str_mv Sanchez Vasquez, Maria Jose
Bastos Claros, Carlos Alberto
dc.contributor.advisor.spa.fl_str_mv Triana Martínez, Jenniffer Carolina
dc.contributor.author.spa.fl_str_mv Sanchez Vasquez, Maria Jose
Bastos Claros, Carlos Alberto
dc.subject.es_ES.fl_str_mv Osteoartritis
SVM
Aprendizaje de máquina
Características Kellgren-Lawrence
Rayos X
topic Osteoartritis
SVM
Aprendizaje de máquina
Características Kellgren-Lawrence
Rayos X
Osteoarthritis
SVM
Machine Learning
Kellgren-Lawrence Features
X- Ray
dc.subject.keyword.es_ES.fl_str_mv Osteoarthritis
SVM
Machine Learning
Kellgren-Lawrence Features
X- Ray
description Propia
publishDate 2020
dc.date.issued.spa.fl_str_mv 2020-12-02
dc.date.accessioned.none.fl_str_mv 2021-03-10T20:24:19Z
dc.date.available.none.fl_str_mv 2021-03-10T20:24:19Z
dc.type.spa.fl_str_mv Trabajo de grado (Pregrado y/o Especialización)
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_7a1f
dc.type.coarversion.none.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
format http://purl.org/coar/resource_type/c_7a1f
dc.identifier.uri.none.fl_str_mv http://repositorio.uan.edu.co/handle/123456789/3159
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Arregui Espinoza JM, Y. A. (2016). Utilidad de rayos x digital en el diagnóstico de artrosis de rodilla en pacientes de 50 a 60 años de edad en el Hospital Privado Northospital de la ciudad de Quito. Quito: UCE.
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Capapé, D. D. (2020). Cirugía Ortopédica y Traumatología Deportiva. Retrieved from Artrosis de rodilla (Gonartrosis): http://doctorlopezcapape.com/cirugia-ortopedica/artrosis-de-rodilla- gonartrosis
Cardona H.D.V, O. Á. (2014). Automatic Recognition of Microcalcifications in Mammography Images through Fractal Texture Analysis. Springer, Cham, Lecture Notes in Computer Science, vol 8888.
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Chen, P. (2018, 09 03). Knee Osteoarthritis Severity Grading Dataset. Retrieved from Mendeley: https://data.mendeley.com/datasets/56rmx5bjcr/1
Cheng-Jin Du, D.-W. S. (2008). Histogram Equalization. ScienceDirect.
Dhabhai, A. K. (2016). Empirical Study of Image Classification Techniques to Classify the Image using SVM: A Review. International Journal of Innovative Research in Computer and Communication Engineering, pp. 1-6.
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Farias Concha NM. (2011, 12). MÁQUINAS VECTORIALES HÍBRIDAS PARA CLASIFICAR ACCIDENTES DE TRANSITO EN LA REGION METROPOLITANA . Retrieved from Pontificia Universidad Catolica de Valparaiso : http://opac.pucv.cl/pucv_txt/Txt- 9500/UCF9980_01.pdf
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Guermazi, A. D. (2015). Severe radiographic knee osteoarthritis – does Kellgren and Lawrence grade 4 represent end stage disease? – the MOST study. Osteoarthritis and Cartilage, pp. 1499-1505.
Guo H, W. W. (2009). A novel learning model-Kernel Granular Support Vector Machine. International Conference on Machine Learning and Cybernetics, pp. 930-935.
Guyon I, G. S. (2008). Freature Extraction Foundation and Aplications. Poland: Springer.
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Hertzmann A, F. D. (2015). Support Vector Machines. Retrieved from http://www.cs.toronto.edu/~mbrubake/teaching/C11/Handouts/SupportVectorMachines.pdf
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dc.identifier.instname.spa.fl_str_mv instname:Universidad Antonio Nariño
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional UAN
dc.identifier.repourl.spa.fl_str_mv repourl:https://repositorio.uan.edu.co/
url http://repositorio.uan.edu.co/handle/123456789/3159
identifier_str_mv Agarap FA. (2018). A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data. 10th International Conference on Machine Learning and Computing, pp. 26-30.
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Arregui Espinoza JM, Y. A. (2016). Utilidad de rayos x digital en el diagnóstico de artrosis de rodilla en pacientes de 50 a 60 años de edad en el Hospital Privado Northospital de la ciudad de Quito. Quito: UCE.
Bradley J. Erickson, P. K. (2017). Machine Learning for Medical Imaging. RadioGraphics, vol. 37, no. 2.
Braun, H. a. (2012). Diagnosis of osteoarthritis: Imaging. Bone, pp. 278-288.
C. Cortes and V. Vapnik. (1995). Support-Vector Networks, Machine Learning. Springer, pp. 273- 297.
C. Wang, L. L. (2011). Face Recognition Based on Principle Component Analysis and Support Vector Machine. 3rd International Workshop on Intelligent Systems and Applications, pp.1-4.
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Capapé, D. D. (2020). Cirugía Ortopédica y Traumatología Deportiva. Retrieved from Artrosis de rodilla (Gonartrosis): http://doctorlopezcapape.com/cirugia-ortopedica/artrosis-de-rodilla- gonartrosis
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Carmona Suarez, E. (2016). Tutorial sobre Máquinas de Vectores Soporte (SVM). Universidad Nacional de Educacion a Distancia (UNED), Madrid- España, pp. 1-27.
Cartas Solis U, P. H. (2015). Demography broad in the knees osteoarthritis. Revista Cubana de Reumatología, vol.17 no.1.
Chen, P. (2018, 09 03). Knee Osteoarthritis Severity Grading Dataset. Retrieved from Mendeley: https://data.mendeley.com/datasets/56rmx5bjcr/1
Cheng-Jin Du, D.-W. S. (2008). Histogram Equalization. ScienceDirect.
Dhabhai, A. K. (2016). Empirical Study of Image Classification Techniques to Classify the Image using SVM: A Review. International Journal of Innovative Research in Computer and Communication Engineering, pp. 1-6.
Dhabhai. A, K. G. (2016). Empirical Study of Image Classification Techniques to Classify the Image using SVM: A Review. International Journal of Innovative Research in Computer and Communication Engineering, pp. 1-6.
drzezo. (2017, 04 20). Imaging for osteoarthritis. Retrieved from PHYSICAL MEDICINE & REHABILITATION : https://musculoskeletalkey.com/imaging-for-osteoarthritis/
Farias Concha NM. (2011, 12). MÁQUINAS VECTORIALES HÍBRIDAS PARA CLASIFICAR ACCIDENTES DE TRANSITO EN LA REGION METROPOLITANA . Retrieved from Pontificia Universidad Catolica de Valparaiso : http://opac.pucv.cl/pucv_txt/Txt- 9500/UCF9980_01.pdf
Felson, D. (1988). Epidemiology of hip and knee osteoarthritis. Oxford Journals, pp.1-28.
Fierro J. (01 de 05 de 2020). Director medico de la Clinica Medilaser de Neiva. (I. d. grado, Entrevistador)
Flandry, F. M. (2011). Normal Anatomy and Biomechanics of the Knee. Sports Medicine and Arthroscopy Review, pp. 82-92.
FULKERSON J P, G. H. (1980). Anatomy of the Knee Joint Lateral Retinaculum. Clinical Orthopaedics and Related Research, pp. 183-188.
Garcia Balboa, J. F. (2018). Homogeneity Test for Confusion Matrices: A Method and an Example. IEEE International Geoscience and Remote Sensing Symposium, pp. 1203-1205.
Garcia-Balboa, J. A.-F.-L.-A. (2018). Homogeneity Test for Confusion Matrices: A Method and an Example. IEEE International Geoscience and Remote Sensing Symposium, pp. 1203-1205
Gavrilov Z. (n.d.). SVM Tutorial. Retrieved from https://web.mit.edu/zoya/www/SVM.pdf
Gonzalez R, B. A. (2017). Application of Support Vector Machines (SVM) for clinical diagnosis of Parkinson's Disease and Essential Tremor. Revista Iberoamericana de Automática e Informática Industrial RIAI, pp. 394-405.
Guermazi, A. D. (2015). Severe radiographic knee osteoarthritis – does Kellgren and Lawrence grade 4 represent end stage disease? – the MOST study. Osteoarthritis and Cartilage, pp. 1499-1505.
Guo H, W. W. (2009). A novel learning model-Kernel Granular Support Vector Machine. International Conference on Machine Learning and Cybernetics, pp. 930-935.
Guyon I, G. S. (2008). Freature Extraction Foundation and Aplications. Poland: Springer.
Haidekker, M. A. (2011). Advanced Biomedical Image Analysis. New Jersey: John Wiley & Sons. Inc
HEALTH, S. C. (2020). Lesiones de ligamento de la rodilla. Retrieved from STANFORD CHILDREN'S HEALTH: https://www.stanfordchildrens.org/es/topic/default?id=ligamentinjuriestotheknee-85-P04023
Hertzmann A, F. D. (2015). Support Vector Machines. Retrieved from http://www.cs.toronto.edu/~mbrubake/teaching/C11/Handouts/SupportVectorMachines.pdf
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spelling Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0)Acceso abiertohttps://creativecommons.org/licenses/by-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Triana Martínez, Jenniffer CarolinaSanchez Vasquez, Maria JoseBastos Claros, Carlos Alberto10753123241075311566382122332021-03-10T20:24:19Z2021-03-10T20:24:19Z2020-12-02http://repositorio.uan.edu.co/handle/123456789/3159Agarap FA. (2018). A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data. 10th International Conference on Machine Learning and Computing, pp. 26-30.Alam S, K. M.-Y. (2016). Performance of classification based on PCA, linear SVM, and Multi-kernel SVM. Eighth International Conference on Ubiquitous and Future Networks (ICUFN), pp. 987- 989.Amat Rodrigo J. (2017, 04). Máquinas de Vector Soporte (Support Vector Machines, SVMs). 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Curvilinear thresholding method for noisy images based on 2D histogram. IEEE International Conference on Robotics and Biomimetics, pp. 1014-1019.HUAN-JUN, L. y.-N.-F. (2005). A METHOD TO CHOOSE KERNEL FUNCTION AND ITS PARAMETERS FOR SUPPORT VECTOR MACHINES. Fourth International Conference on Machine Learning and CyberneticS, pp. 4277-4280.IArtificial.net. (n.d.). Retrieved from https://www.iartificial.net/maquinas-de-vectores-de-soporte- svm/#:~:text=El%20truco%20del%20kernel%20consiste,con%20una%20superficie%20de% 20decisi%C3%B3nJakkula V. (2006). Tutorial on Support Vector Machine (SVM). Washington State: School of EECS, Washington State University.Khalid, R. R. (2015). Enhanced dynamic quadrant histogram equalization plateau limit for image contrast enhancement. Fifth InternationalConference on Digital Information and Communication Technology and its Applications (DICTAP), pp. 86-91.Kim, K. G. (2016). Deep Learning. Healtcare Informatics Research, pp. 351-354.Koonsanit, K. T. (2017). Image enhancement on digital x-ray images using N-CLAHE. Biomedical Engineering International Conference (BMEiCON), pp. 1-4.Kwang Gi, K. (2016). Deep Learning. Healtcare Informatics Research, pp. 351-354.Lopez Diaz, A. (2018). Fundamentos Matematicos de los Metodos Kernel para Aprendizaje Supervisado. Universidad de Sevilla. DEPARTAMENTO: CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL, pp. 1-73.López Pineda G. (2017, 05). Modelos de regresión para datos funcionales por la metodología de Kernel reproductor en espacios de Hilbert. Retrieved from BUAP: https://repositorioinstitucional.buap.mx/handle/20.500.12371/488López-Portilla Vigil, B. M. (2016). Implementación del Algoritmo de Otsu sobre FPGA. Revista Cubana de Ciencias Informáticas. Retrieved from http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S2227- 18992016000300002&lng=es&tlng=es.Lovejoy, C. (2007). The natural history of human gait and posture: Part 3. The knee. Gait & Posture, pp. 325-341.Luijkx, T. &. (2016). Kellgren and Lawrence system for classification of osteoarthritis of knee. Retrieved from http://radiopaedia. org/articles/kellgren-and-lawrencesystem-for- classification-of-osteoarthritis-of-kneeMark D. Kohn, A. A. (2016). Classifications in Brief: Kellgren-Lawrence Classification of Osteoarthritis. Clinical Orthopaedics and Related Research, pp. 1886-1893.Martinez Figueroa R, M. F. (2017). Knee Osteoarthritis (osteoarthrosis). Revista Chilena de Ortopedia y Traumatología, pp. 45-51.Martinez, D. A. (2020, 05 28). Caracteristicas mas relevantes de la rodilla. (M. J. Bastos, Interviewer)Martínez, V. G. (2013). TÉCNICAS DE UMBRALIZACIÓN PARA LA DETECCIÓN DE ANOMALÍAS EN LA PARED AÓRTICA MEDIANTE OCT. UNIVERSIDAD DE CANTABRIA.Matlab. (2020). MathWorks. Retrieved from regionprops: https://www.mathworks.com/help/images/ref/regionprops.htmlMatlab. (2020). MathWorks. 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INC.instname:Universidad Antonio Nariñoreponame:Repositorio Institucional UANrepourl:https://repositorio.uan.edu.co/PropiaThe purpose of this project is to implement a Support Vector Machine (SVM) classifier, based on the Kellgren-Lawrence (KL) grade classification method, and the use of X-ray images (XR), with the objective of supporting the trauma specialist's diagnosis in the detection of knee Osteoarthritis (OA) grade according to the above-mentioned classification, in Orthopedic and Traumatology of the Medilaser Clinic of Neiva treated between the months of June and August 2020. It is expected that this project will allow the categorization of the degree of Osteoarthritis (OA) of the knee supporting the diagnosis of the specialist, in such a way that the amount of tests in addition to those previously named is minimized to determine a diagnosis of this pathology.El propósito de este proyecto es implementar un clasificador de Máquina de Vectores de Soporte (SVM), basándose en el método de clasificación de la Escala de Kellgren-Lawrence (KL), y la utilización de imágenes de rayos x (RX), con el objetivo de apoyar en el diagnóstico del especialista en traumatología en la detección del grado Osteoartritis (OA) de rodilla de acuerdo a la clasificación antes mencionada, en pacientes de Ortopedia y Traumatología de la Clínica Medilaser de Neiva tratados entre los meses de junio y agosto de 2020. Se espera que este proyecto permita categorizar el grado de Osteoartritis (OA) de rodilla apoyando el diagnóstico del especialista, de tal manera que se minimice la cantidad de pruebas además de las nombradas anteriormente para determinar un diagnóstico de esta patología.OtroIngeniero(a) Electrónico(a)PregradoFinanciación estudiantes 2'270.000 COP, Financiación UAN 1'009.520 COPPresencialspaUniversidad Antonio NariñoIngeniería ElectrónicaFacultad de Ingeniería Mecánica, Electrónica y BiomédicaNeiva BuganvilesOsteoartritisSVMAprendizaje de máquinaCaracterísticas Kellgren-LawrenceRayos XOsteoarthritisSVMMachine LearningKellgren-Lawrence FeaturesX- RayClasificador de Máquinas de Vectores de Soporte para el Apoyo en la Detección del Grado I y II de Osteoartritis de Rodilla Según Kellgren- Lawrence Mediante Imágenes de Rayos X.Trabajo de grado (Pregrado y/o Especialización)http://purl.org/coar/resource_type/c_7a1fhttp://purl.org/coar/version/c_970fb48d4fbd8a85ORIGINAL2020MariaJoseSanchezVasquez.pdf2020MariaJoseSanchezVasquez.pdfTrabajo de gradoapplication/pdf2328980https://repositorio.uan.edu.co/bitstreams/d7ee6a55-7808-4593-88eb-badb1319f4e6/download8fd33611845305c5880443b4c2e9e27dMD5172020AutorizaciondeAutores2.pdf2020AutorizaciondeAutores2.pdfAutorización de Autoresapplication/pdf367575https://repositorio.uan.edu.co/bitstreams/2c2710ba-005f-4a6b-8c23-197b3d8576d3/download2cb870f8f7e57dfa91c0a6f67e06815dMD5182020AutorizaciondeAutores.pdf2020AutorizaciondeAutores.pdfAutorización de Autoresapplication/pdf340528https://repositorio.uan.edu.co/bitstreams/1d02fb17-ae35-4104-b0df-0bccc71e18ab/downloaded81d1ad05ff8a9fd7c156464a8b8d83MD519CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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