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dc.contributor.advisorTalero Sarmiento, Leonardo Hernán
dc.contributor.advisorParra Sánchez, Diana Teresa
dc.contributor.advisorMoreno Corzo, Feisar Enrique
dc.contributor.authorConsuegra Rodríguez, Juan Felipe
dc.contributor.authorHernández Suárez, Yeison Omar
dc.coverage.spatialColombiaspa
dc.date.accessioned2022-01-25T12:51:15Z
dc.date.available2022-01-25T12:51:15Z
dc.date.issued2021-05-18
dc.identifier.urihttp://hdl.handle.net/20.500.12749/15357
dc.description.abstractEl cáncer de próstata representa el tipo de cáncer más común en hombres colombianos, adicionalmente, es la segunda causa de muertes masculinas por cáncer. La mejor manera de poder confirmar la existencia de células malignas en la próstata es mediante el análisis de resonancias magnéticas y posterior toma de biopsia transrectal; pero, debido al tipo de examen, existe la posibilidad de complicaciones posteriores a la toma de muestras en pacientes sometidos a biopsia de próstata, desde sangrado y dolor pélvico hasta sepsis (respuesta inflamatoria generalizada) debido a infección. Para brindar una solución a esta problemática, se plantea una idea de trabajo de grado alineado al objetivo de desarrollo sostenible (ODS) número 3: Salud y Bienestar. El objetivo de este proyecto es el desarrollo de un modelo clasificador que sugiera la presencia de cáncer prostático en pacientes con sospecha de malignidad sin la necesidad de toma de biopsia mediante el reconocimiento de imágenes. Para ello se entrenó una red neuronal convolucional con imágenes diagnósticas (particularmente, con resonancias magnéticas), puesto que las redes neuronales artificiales han sido usadas en diversas áreas para resolver problemas de clasificación de imágenes, como en salud para diagnóstico de cáncer de mama y para el diagnóstico asistido por ordenador de retinopatía. Para este desarrollo se inició con la búsqueda de un conjunto de datos de imágenes diagnósticas de cáncer de próstata correctamente etiquetadas, clasificadas y documentadas por expertos que lograran entrenar una red neuronal especializada en clasificación de imágenes. Seguido a esto se definió un modelo de entrenamiento para observar el desempeño de dos redes neuronales especializadas en clasificación, con base en los resultados se hizo una elección de una de estas dos redes neuronales. Finalmente se desarrolló un prototipo de software web que ofrece a sus usuarios la posibilidad de clasificar imágenes de próstata como clínicamente significativas y no significativas. Adicionalmente permite a los usuarios almacenar sus resultados en una base de datos, además de visualizar y/o convertir sus imágenes médicas.spa
dc.description.tableofcontents1. INTRODUCCIÓN ............................................................................................ 13 2. PLANTEAMIENTO DEL PROBLEMA ............................................................. 14 2.1 JUSTIFICACIÓN ...................................................................................... 15 3. OBJETIVOS ...................................................................................................... 16 3.1 OBJETIVO GENERAL .................................................................................... 16 3.2 OBJETIVOS ESPECÍFICOS ........................................................................... 16 4 MARCO REFERENCIAL .................................................................................... 17 4.1 MARCO CONCEPTUAL ................................................................................. 17 4.1.1 PROSTATE CANCER .................................................................................. 17 4.1.2 COMPUTER VISION .................................................................................... 17 4.1.3 PATTERN RECOGNITION .......................................................................... 17 4.1.4 MACHINE LEARNING ................................................................................. 17 4.2 MARCO TEÓRICO .......................................................................................... 17 4.2.1 EXAMEN FÍSICO PARA DETECCIÓN DE CÁNCER DE PRÓSTATA ........ 18 4.2.2 ANTÍGENO PROSTÁTICO ESPECÍFICO EN SANGRE .............................. 18 4.2.3 BIOPSIA DE PRÓSTATA ............................................................................. 18 4.2.4 CLASIFICACIÓN DEL CÁNCER DE PRÓSTATA ........................................ 19 4.2.5 RESONANCIA MAGNÉTICA ....................................................................... 19 4.2.6 RESONANCIA MAGNÉTICA MULTIPARAMÉTRICA .................................. 19 4.2.7 VISIÓN POR COMPUTADOR Y REDES NEURONALES CONVOLUCIONALES .............................................................................................................................. 21 4.2.8 PROCESAMIENTO DE IMÁGENES ............................................................ 22 4.3 ESTADO DEL ARTE ....................................................................................... 29 4.4 MARCO LEGAL .............................................................................................. 39 5. METODOLOGÍA................................................................................................ 41 6. DESARROLLO DEL PROYECTO ..................................................................... 43 6.1 CONSTRUCCIÓN DE UN DATASET DE IMÁGENES DIAGNÓSTICAS DE CÁNCER DE PRÓSTATA ..................................................................................... 43 6.1.1 QIN-PROSTATE-REPEATABILITY .............................................................. 44 6.1.2 PROSTATE-MRI .......................................................................................... 44 6.1.3 SPIE-AAPM-NCI PROSTATEX CHALLENGES ........................................... 45 6.1.4 INFORMACIÓN UTILIZADA DE PROSTATEX ............................................ 46 6.1.5 CLASIFICACIÓN CLÍNICAMENTE SIGNIFICATIVA ................................... 46 6.1.6 ELECCIÓN DE UN PLANO DE TOMA DE IMAGEN ................................... 47 6.1.7 TRANSFORMACIÓN DE IMÁGENES ......................................................... 48 6.1.8 CONJUNTO DE DATOS RESULTANTE ..................................................... 48 6.2 ADAPTACIÓN DE UN MODELO PARA SUGERIR LA EXISTENCIA DE CÁNCER PROSTÁTICO ....................................................................................................... 48 6.2.1 SELECCIÓN, LIMPIEZA Y TRATAMIENTO DE DATOS ............................. 49 6.2.2 PREPROCESAMIENTO DE DATOS ........................................................... 50 6.2.3 DISEÑO DEL ENTRENAMIENTO ................................................................ 50 6.2.4 REDES NEURONALES CONSULTADAS Y RESULTADOS ....................... 51 6.2.5 MATRIZ DE CONFUSIÓN ........................................................................... 55 6.2.6 ELECCIÓN DE UNA RED NEURONAL ....................................................... 56 6.3 DISEÑAR UN PROTOTIPO FUNCIONAL DE SOFTWARE ........................... 56 6.3.1 PLAN DE DESARROLLO DE SOFTWARE ................................................. 56 6.3.2 DEFINICIÓN DE ACTORES EN EL SISTEMA WEB ................................... 56 6.3.3 CASOS DE USO .......................................................................................... 58 6.3.4 REQUERIMIENTOS DE SOFTWARE ......................................................... 58 6.3.5 SECUENCIA DEL PROTOTIPO .................................................................. 59 7. CONCLUSIONES .............................................................................................. 61 8. RECOMENDACIONES ..................................................................................... 62 9. REFERENCIAS ................................................................................................. 63spa
dc.format.mimetypeapplication/pdfspa
dc.language.isospaspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/*
dc.titlePrototipo funcional de software para la clasificación de imágenes diagnósticas en el análisis asistido de cáncer de próstata implementando redes neuronales convolucionalesspa
dc.title.translatedFunctional software prototype for the classification of diagnostic images in the assisted analysis of prostate cancer by implementing convolutional neural networksspa
dc.degree.nameIngeniero de Sistemasspa
dc.publisher.grantorUniversidad Autónoma de Bucaramanga UNABspa
dc.rights.localAbierto (Texto Completo)spa
dc.publisher.facultyFacultad Ingenieríaspa
dc.publisher.programPregrado Ingeniería de Sistemasspa
dc.description.degreelevelPregradospa
dc.type.driverinfo:eu-repo/semantics/bachelorThesis
dc.type.localTrabajo de Gradospa
dc.type.coarhttp://purl.org/coar/resource_type/c_7a1f
dc.subject.keywordsSystems engineerspa
dc.subject.keywordsTechnological innovationsspa
dc.subject.keywordsMachine learningspa
dc.subject.keywordsConvolutional neural networkspa
dc.subject.keywordsProstate cancerspa
dc.subject.keywordsPrototype developmentspa
dc.subject.keywordsArtificial intelligencespa
dc.subject.keywordsElectronic data processingspa
dc.subject.keywordsSoftware developmentspa
dc.identifier.instnameinstname:Universidad Autónoma de Bucaramanga - UNABspa
dc.identifier.reponamereponame:Repositorio Institucional UNABspa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersion
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
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dc.contributor.cvlacTalero Sarmiento, Leonardo Hernán [0000031387]spa
dc.contributor.cvlacMoreno Corzo, Feisar Enrique [0001499008]spa
dc.contributor.cvlacParra Sánchez, Diana Teresa [0001476224]spa
dc.contributor.googlescholarMoreno Corzo, Feisar Enrique [es&oi=ao]spa
dc.contributor.googlescholarParra Sánchez, Diana Teresa [es&oi=ao]spa
dc.contributor.orcidTalero Sarmiento, Leonardo Hernán [0000-0002-4129-9163]spa
dc.contributor.orcidMoreno Corzo, Feisar Enrique [0000-0002-5007-3422]spa
dc.contributor.orcidParra Sánchez, Diana Teresa [0000-0002-7649-0849]spa
dc.contributor.scopusParra Sánchez, Diana Teresa [57195677014]spa
dc.contributor.researchgateTalero Sarmiento, Leonardo Hernán [Leonardo-Talero]spa
dc.contributor.researchgateMoreno Corzo, Feisar Enrique [Feisar-Enrique-Moreno-Corzo-2169498891]spa
dc.contributor.researchgateParra Sánchez, Diana Teresa [Diana-Parra-Sanchez-2]spa
dc.subject.lembIngeniería de sistemasspa
dc.subject.lembInnovaciones tecnológicasspa
dc.subject.lembDesarrollo de prototiposspa
dc.subject.lembInteligencia artificialspa
dc.subject.lembProcesamiento electrónico de datosspa
dc.subject.lembDesarrollo de softwarespa
dc.identifier.repourlrepourl:https://repository.unab.edu.cospa
dc.description.abstractenglishProstate cancer represents the most common type of cancer in Colombian men; additionally, it is the second cause of male cancer deaths. The best way to confirm the existence of malignant cells in the prostate is through the analysis of magnetic resonance imaging and subsequent transrectal biopsy; but, due to the type of examination, there is the possibility of complications following the sampling in patients undergoing prostate biopsy, from bleeding and pelvic pain to sepsis (generalized inflammatory response) due to infection. In order to provide a solution to this problem, a degree project idea aligned to the Sustainable Development Goal (ODS) number 3: Health and Well-being is proposed. The reason for this project is the development of a classifier model that suggests the presence of prostate cancer in patients with suspected malignancy without the need for biopsy through image recognition. For this purpose, a convolutional neural network will be trained with diagnostic images (particularly magnetic resonance imaging), since artificial neural networks have been used in several areas to solve image classification problems, such as in health for breast cancer diagnosis and for computerassisted diagnosis of retinopathy. This development began with the search for a dataset of diagnostic images of prostate cancer correctly labeled, classified and documented by experts who were able to train a neural network specialized in image classification. Following this, a training model was defined to observe the performance of two neural networks specialized in classification, based on the results a choice of one of these two neural networks was made. Finally, a prototype web software was developed that offers its users the possibility to classify prostate images as clinically significant and nonsignificant. Additionally, it allows users to store their results in a database, as well as to visualize and/or convert their medical images.spa
dc.subject.proposalAprendizaje automáticospa
dc.subject.proposalRed neuronal convolucionalspa
dc.subject.proposalCáncer de próstataspa
dc.subject.proposalSmall VGG NETspa
dc.subject.proposalInception V3spa
dc.type.redcolhttp://purl.org/redcol/resource_type/TP
dc.rights.creativecommonsAtribución-NoComercial-SinDerivadas 2.5 Colombia*
dc.coverage.campusUNAB Campus Bucaramangaspa
dc.description.learningmodalityModalidad Presencialspa


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