Prototipo funcional de software para la clasificación de imágenes diagnósticas en el análisis asistido de cáncer de próstata implementando redes neuronales convolucionales
El 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...
- Autores:
-
Consuegra Rodríguez, Juan Felipe
Hernández Suárez, Yeison Omar
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2021
- Institución:
- Universidad Autónoma de Bucaramanga - UNAB
- Repositorio:
- Repositorio UNAB
- Idioma:
- spa
- OAI Identifier:
- oai:repository.unab.edu.co:20.500.12749/15357
- Acceso en línea:
- http://hdl.handle.net/20.500.12749/15357
- Palabra clave:
- Systems engineer
Technological innovations
Machine learning
Convolutional neural network
Prostate cancer
Prototype development
Artificial intelligence
Electronic data processing
Software development
Ingeniería de sistemas
Innovaciones tecnológicas
Desarrollo de prototipos
Inteligencia artificial
Procesamiento electrónico de datos
Desarrollo de software
Aprendizaje automático
Red neuronal convolucional
Cáncer de próstata
Small VGG NET
Inception V3
- Rights
- License
- http://creativecommons.org/licenses/by-nc-nd/2.5/co/
| id |
UNAB2_7395cb17214edfbd190e9c29ed1622a3 |
|---|---|
| oai_identifier_str |
oai:repository.unab.edu.co:20.500.12749/15357 |
| network_acronym_str |
UNAB2 |
| network_name_str |
Repositorio UNAB |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
Prototipo funcional de software para la clasificación de imágenes diagnósticas en el análisis asistido de cáncer de próstata implementando redes neuronales convolucionales |
| dc.title.translated.spa.fl_str_mv |
Functional software prototype for the classification of diagnostic images in the assisted analysis of prostate cancer by implementing convolutional neural networks |
| title |
Prototipo funcional de software para la clasificación de imágenes diagnósticas en el análisis asistido de cáncer de próstata implementando redes neuronales convolucionales |
| spellingShingle |
Prototipo funcional de software para la clasificación de imágenes diagnósticas en el análisis asistido de cáncer de próstata implementando redes neuronales convolucionales Systems engineer Technological innovations Machine learning Convolutional neural network Prostate cancer Prototype development Artificial intelligence Electronic data processing Software development Ingeniería de sistemas Innovaciones tecnológicas Desarrollo de prototipos Inteligencia artificial Procesamiento electrónico de datos Desarrollo de software Aprendizaje automático Red neuronal convolucional Cáncer de próstata Small VGG NET Inception V3 |
| title_short |
Prototipo funcional de software para la clasificación de imágenes diagnósticas en el análisis asistido de cáncer de próstata implementando redes neuronales convolucionales |
| title_full |
Prototipo funcional de software para la clasificación de imágenes diagnósticas en el análisis asistido de cáncer de próstata implementando redes neuronales convolucionales |
| title_fullStr |
Prototipo funcional de software para la clasificación de imágenes diagnósticas en el análisis asistido de cáncer de próstata implementando redes neuronales convolucionales |
| title_full_unstemmed |
Prototipo funcional de software para la clasificación de imágenes diagnósticas en el análisis asistido de cáncer de próstata implementando redes neuronales convolucionales |
| title_sort |
Prototipo funcional de software para la clasificación de imágenes diagnósticas en el análisis asistido de cáncer de próstata implementando redes neuronales convolucionales |
| dc.creator.fl_str_mv |
Consuegra Rodríguez, Juan Felipe Hernández Suárez, Yeison Omar |
| dc.contributor.advisor.none.fl_str_mv |
Talero Sarmiento, Leonardo Hernán Parra Sánchez, Diana Teresa Moreno Corzo, Feisar Enrique |
| dc.contributor.author.none.fl_str_mv |
Consuegra Rodríguez, Juan Felipe Hernández Suárez, Yeison Omar |
| dc.contributor.cvlac.spa.fl_str_mv |
Talero Sarmiento, Leonardo Hernán [0000031387] Moreno Corzo, Feisar Enrique [0001499008] Parra Sánchez, Diana Teresa [0001476224] |
| dc.contributor.googlescholar.spa.fl_str_mv |
Moreno Corzo, Feisar Enrique [es&oi=ao] Parra Sánchez, Diana Teresa [es&oi=ao] |
| dc.contributor.orcid.spa.fl_str_mv |
Talero Sarmiento, Leonardo Hernán [0000-0002-4129-9163] Moreno Corzo, Feisar Enrique [0000-0002-5007-3422] Parra Sánchez, Diana Teresa [0000-0002-7649-0849] |
| dc.contributor.scopus.spa.fl_str_mv |
Parra Sánchez, Diana Teresa [57195677014] |
| dc.contributor.researchgate.spa.fl_str_mv |
Talero Sarmiento, Leonardo Hernán [Leonardo-Talero] Moreno Corzo, Feisar Enrique [Feisar-Enrique-Moreno-Corzo-2169498891] Parra Sánchez, Diana Teresa [Diana-Parra-Sanchez-2] |
| dc.subject.keywords.spa.fl_str_mv |
Systems engineer Technological innovations Machine learning Convolutional neural network Prostate cancer Prototype development Artificial intelligence Electronic data processing Software development |
| topic |
Systems engineer Technological innovations Machine learning Convolutional neural network Prostate cancer Prototype development Artificial intelligence Electronic data processing Software development Ingeniería de sistemas Innovaciones tecnológicas Desarrollo de prototipos Inteligencia artificial Procesamiento electrónico de datos Desarrollo de software Aprendizaje automático Red neuronal convolucional Cáncer de próstata Small VGG NET Inception V3 |
| dc.subject.lemb.spa.fl_str_mv |
Ingeniería de sistemas Innovaciones tecnológicas Desarrollo de prototipos Inteligencia artificial Procesamiento electrónico de datos Desarrollo de software |
| dc.subject.proposal.spa.fl_str_mv |
Aprendizaje automático Red neuronal convolucional Cáncer de próstata Small VGG NET Inception V3 |
| description |
El 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. |
| publishDate |
2021 |
| dc.date.issued.none.fl_str_mv |
2021-05-18 |
| dc.date.accessioned.none.fl_str_mv |
2022-01-25T12:51:15Z |
| dc.date.available.none.fl_str_mv |
2022-01-25T12:51:15Z |
| dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/bachelorThesis |
| dc.type.local.spa.fl_str_mv |
Trabajo de Grado |
| dc.type.coar.none.fl_str_mv |
http://purl.org/coar/resource_type/c_7a1f |
| dc.type.redcol.none.fl_str_mv |
http://purl.org/redcol/resource_type/TP |
| format |
http://purl.org/coar/resource_type/c_7a1f |
| dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/20.500.12749/15357 |
| dc.identifier.instname.spa.fl_str_mv |
instname:Universidad Autónoma de Bucaramanga - UNAB |
| dc.identifier.reponame.spa.fl_str_mv |
reponame:Repositorio Institucional UNAB |
| dc.identifier.repourl.spa.fl_str_mv |
repourl:https://repository.unab.edu.co |
| url |
http://hdl.handle.net/20.500.12749/15357 |
| identifier_str_mv |
instname:Universidad Autónoma de Bucaramanga - UNAB reponame:Repositorio Institucional UNAB repourl:https://repository.unab.edu.co |
| dc.language.iso.spa.fl_str_mv |
spa |
| language |
spa |
| dc.relation.references.spa.fl_str_mv |
A. R. Syafeeza, M. Khalil-Hani, N. M. Saad, F. Salehuddin, H. N. A. (2015). An Improved Retraining Scheme for Convolutional Neural Network. Journal of Telecommunication, Electronic and Computer Engineering, Vol. 7. Abbasi, A. A., Hussain, L., Awan, I. A., Abbasi, I., Majid, A., Nadeem, M. S. A., & Chaudhary, Q.-A. (2020). Detecting prostate cancer using deep learning convolution neural network with transfer learning approach. Cognitive Neurodynamics, 14(4), 523–533. https://doi.org/10.1007/s11571-020-09587-5 Acharya, T., & Ray, A. K. (n.d.). Image Processing Principles and Applications @ Z E i C I E N C E. Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., Van Esesn, B. C., Awwal, A. A. S., & Asari, V. K. (2018). The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches. Alqahtani, S., Wei, C., Zhang, Y., Szewczyk-Bieda, M., Wilson, J., Huang, Z., & Nabi, G. (2020). Prediction of prostate cancer Gleason score upgrading from biopsy to radical prostatectomy using pre-biopsy multiparametric MRI PIRADS scoring system. Scientific Reports, 10(1), 7722. https://doi.org/10.1038/s41598-020-64693-y American Cancer Society. (2017). Prostate Cancer Early Detection , Diagnosis , and Staging Can Ovarian Cancer Be Found Early ? American Cancer Society, 1–25. https://www.cancer.org/cancer/prostate-cancer/detection-diagnosis-staging.html American College of Radiology. (2019). PI-RADS v2.1 - ACR Aszemi, N. M., & Dominic, P. D. . (2019). Hyperparameter Optimization in Convolutional Neural Network using Genetic Algorithms. International Journal of Advanced Computer Science and Applications, 10(6). https://doi.org/10.14569/IJACSA.2019.0100638 Balagourouchetty, L., Pragatheeswaran, J. K., Pottakkat, B., & Ramkumar, G. (2020). GoogLeNet-Based Ensemble FCNet Classifier for Focal Liver Lesion Diagnosis. IEEE Journal of Biomedical and Health Informatics, 24(6), 1686–1694. https://doi.org/10.1109/JBHI.2019.2942774 Bosch, A. van den, Hengst, B., Lloyd, J., Miikkulainen, R., Blockeel, H., & Blockeel, H. (2011). Holdout Evaluation. In Encyclopedia of Machine Learning (pp. 506–507). Springer US. https://doi.org/10.1007/978-0-387-30164-8_369 Cabaneros, S. M., Calautit, J. K., & Hughes, B. R. (2019). A review of artificial neural network models for ambient air pollution prediction. Environmental Modelling & Software, 119, 285– 304. https://doi.org/10.1016/j.envsoft.2019.06.014 Cancelas, J. A., González, R. C., Álvarez, I., & Enguita, J. M. (2016). Procesamiento Morfológico, Visión 3D: Estereoscopía, Álgebra lineal básica para visión por computador, Geometría Proyectiva para Visión 3D. In Conceptos y Métodos en Visión Por Computador (Vol. 1). Cao, R., Mohammadian Bajgiran, A., Afshari Mirak, S., Shakeri, S., Zhong, X., Enzmann, D., Raman, S., & Sung, K. (2019). Joint Prostate Cancer Detection and Gleason Score Prediction in mp-MRI via FocalNet. IEEE Transactions on Medical Imaging, 38(11), 2496– 2506. https://doi.org/10.1109/TMI.2019.2901928 Chen, Q., Hu, S., Long, P., Lu, F., Shi, Y., & Li, Y. (2019). A Transfer Learning Approach for Malignant Prostate Lesion Detection on Multiparametric MRI. Technology in Cancer Research & Treatment, 18, 153303381985836. https://doi.org/10.1177/1533033819858363 Chen, S., Sun, W., Huang, L., Yang, X., & Huang, J. (2019a). Compressing Fully Connected Layers using Kronecker Tensor Decomposition. Proceedings of IEEE 7th International Conference on Computer Science and Network Technology, ICCSNT 2019, 308–312. https://doi.org/10.1109/ICCSNT47585.2019.8962432 Chen, S., Sun, W., Huang, L., Yang, X., & Huang, J. (2019b). Compressing Fully Connected Layers using Kronecker Tensor Decomposition. 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT), 308–312. https://doi.org/10.1109/ICCSNT47585.2019.8962432 Cireşan, D., Meier, U., & Schmidhuber, J. (2012). Multi-column Deep Neural Networks for Image Classification. http://arxiv.org/abs/1202.2745 Çınar, M., Engin, M., Engin, E. Z., & Ziya Ateşçi, Y. (2009). Early prostate cancer diagnosis by using artificial neural networks and support vector machines. Expert Systems with Applications, 36(3), 6357–6361. https://doi.org/10.1016/j.eswa.2008.08.010 Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging, 26(6), 1045–1057. https://doi.org/10.1007/s10278-013-9622-7 Colaboratory. (2021). Colaboratory – Google. https://research.google.com/colaboratory/faq.html#resource-limits Correia, N. A., Batista, L. T. A., Nascimento, R. J. M., Cangussú, M. C. T., Crugeira, P. J. L., Soares, L. G. P., Silveira, L., & Pinheiro, A. L. B. (2020). Detection of prostate cancer by Raman spectroscopy: A multivariate study on patients with normal and altered PSA values. Journal of Photochemistry and Photobiology B: Biology, 204(January), 111801. https://doi.org/10.1016/j.jphotobiol.2020.111801 Costa, D. N., Pedrosa, I., Donato, F., Roehrborn, C. G., & Rofsky, N. M. (2015). MR Imaging– Transrectal US Fusion for Targeted Prostate Biopsies: Implications for Diagnosis and Clinical Management. RadioGraphics, 35(3), 696–708. https://doi.org/10.1148/rg.2015140058 Departamento Nacional de Planeación. (2018). BASES DEL PLAN NACIONAL DE DESARROLLO 2018-2022. Pacto Por Colombia, Pacto Por La Equidad, 861. https://www.dnp.gov.co/DNPN/Paginas/Bases-del-Plan-Nacional-deDesarrollo.aspx%0Ahttps://id.presidencia.gov.co/especiales/190523PlanNacionalDesarrollo/documentos/BasesPND2018-2022.pdf Fedorov, A., Vangel, M. G., Tempany, C. M., & Fennessy, F. M. (2017). Multiparametric Magnetic Resonance Imaging of the Prostate. Investigative Radiology, 52(9), 538–546. https://doi.org/10.1097/RLI.0000000000000382 Fitzmaurice, C., Allen, C., Barber, R. M., Barregard, L., Bhutta, Z. A., Brenner, H., Dicker, D. J., Chimed-Orchir, O., Dandona, R., Dandona, L., Fleming, T., Forouzanfar, M. H., Hancock, J., Hay, R. J., Hunter-Merrill, R., Huynh, C., Hosgood, H. D., Johnson, C. O., Jonas, J. B., … Naghavi, M. (2017). Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-years for 32 Cancer Groups, 1990 to 2015. JAMA Oncology, 3(4), 524. https://doi.org/10.1001/jamaoncol.2016.5688 Ghosh, A. (2019). Artificial Intelligence Using Open Source BI-RADS Data Exemplifying Potential Future Use. Journal of the American College of Radiology, 16(1), 64–72. https://doi.org/10.1016/j.jacr.2018.09.040 Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep Learning. Cambridge: MIT Press, 1. Guo, Y., Ruan, S., Walker, P., & Feng, Y. (2014). Prostate cancer segmentation from multiparametric MRI based on fuzzy Bayesian model. 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 866–869. https://doi.org/10.1109/ISBI.2014.6868008 Jordi Torres. (2018). Deep Learning, Introducción práctica con Keras (PRIMERA PARTE). Kindle Direc. Khan, S., Islam, N., Jan, Z., Ud Din, I., & Rodrigues, J. J. P. C. (2019). A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recognition Letters, 125, 1–6. https://doi.org/10.1016/j.patrec.2019.03.022 Kirlik, G., Gullapalli, R., D’Souza, W., Md Daud Iqbal, G., Naslund, M., Wong, J., Papadimitrou, J., Roys, S., Mistry, N., & Zhang, H. (2018). A Supervised Learning Tool for Prostate Cancer Foci Detection and Aggressiveness Identification using Multiparametric magnetic resonance imaging/magnetic resonance spectroscopy imaging. Cancer Informatics, 17, 117693511878626. https://doi.org/10.1177/1176935118786260 Kitamura, G., & Deible, C. (2020). Retraining an open-source pneumothorax detecting machine learning algorithm for improved performance to medical images. Clinical Imaging, 61, 15– 19. https://doi.org/10.1016/j.clinimag.2020.01.008 Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Neural Information Processing Systems Conference Kumar, V., Abbas, A. K., & Aster, J. C. (2013). Robbins. Patología Humana (Novena ed). Elsevier Litjens, G., Debats, O., Barentsz, J., Karssemeijer, N., & Huisman, H. (2014). Computer-Aided Detection of Prostate Cancer in MRI. IEEE Transactions on Medical Imaging, 33(5), 1083– 1092. https://doi.org/10.1109/TMI.2014.2303821 Liu, S., Zheng, H., Feng, Y., & Li, W. (2017). Prostate cancer diagnosis using deep learning with 3D multiparametric MRI (S. G. Armato & N. A. Petrick (eds.); p. 1013428). https://doi.org/10.1117/12.2277121 Liu, Z., Yang, C., Huang, J., Liu, S., Zhuo, Y., & Lu, X. (2021). Deep learning framework based on integration of S-Mask R-CNN and Inception-v3 for ultrasound image-aided diagnosis of prostate cancer. Future Generation Computer Systems, 114, 358–367. https://doi.org/10.1016/j.future.2020.08.015 Mesrabadi, H. A., & Faez, K. (2018). Improving early prostate cancer diagnosis by using Artificial Neural Networks and Deep Learning. Proceedings - 2018 4th Iranian Conference of Signal Processing and Intelligent Systems, ICSPIS 2018, 39–42. https://doi.org/10.1109/ICSPIS.2018.8700542 Ministerio de Salud y Protección Social. (2018). Observatorio Nacional de Cáncer. ONC Colombia, 1–59 Ministerio de Tecnologías de la Información y las Comunicaciones. (2020). Marco de la Transformación Digital para el Estado Colombiano. 86. https://estrategia.gobiernoenlinea.gov.co/623/articles-149178_recurso_1.pdf Okinda, C., Nyalala, I., Korohou, T., Okinda, C., Wang, J., Achieng, T., Wamalwa, P., Mang, T., & Shen, M. (2020). A review on computer vision systems in monitoring of poultry: A welfare perspective. Artificial Intelligence in Agriculture, 4, 184–208. https://doi.org/10.1016/j.aiia.2020.09.002 Ordieres, J., Limas, M., Ascacibar, F. J., Alba-Elías, F., González-Marcos, A., Pernía-Espinoza, A., & Vergara, E. (2006). Técnicas y algoritmos básicos de visión artificial Recurso electrónico - En línea (Issue December 2016). Organización de las Naciones Unidas. (2015). Objetivos de Desarrollo Sostenible. https://www.un.org/sustainabledevelopment/es/objetivos-de-desarrollo-sostenible/ Parthy, K. (2018). CS231n Convolutional Neural Networks for Visual Recognition. https://cs231n.github.io/convolutional-networks/ Perán Teruel, M., Lorenzo-Gómez, M. F., Veiga Canuto, N., Padilla-Fernández, B. Y., ValverdeMartínez, L. S., Migliorini, F., Jorge Pereira, B., Pires Coelho, H. M., & Osca García, J. M. (2020). Complications of transrectal prostate biopsy in our context. International multicenter study of 3350 patients. Actas Urológicas Españolas (English Edition), 44(3), 196–204. https://doi.org/10.1016/j.acuroe.2020.03.002 Pressman, R. (2002). Ingeniería del Software. Un enfoque práctico. (McGraw-Hill Interamericana (ed.); Séptima). Ramirez Perez, N. A., Vargas, E. G., & Forero Cuellar, O. M. (2019). Supervised Classifiers of Prostate Cancer from Magnetic Resonance Images in T2 Sequences. 2019 14th Iberian Conference on Information Systems and Technologies (CISTI), 1–4. https://doi.org/10.23919/CISTI.2019.8760647 Ray, S. (2019). A Quick Review of Machine Learning Algorithms. 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), 35–39. https://doi.org/10.1109/COMITCon.2019.8862451 Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, RealTime Object Detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779–788. https://doi.org/10.1109/CVPR.2016.91 Sammouda, R., Aboalsamh, H., & Saeed, F. (2015). Comparison between K mean and fuzzy Cmean methods for segmentation of near infrared fluorescent image for diagnosing prostate cancer. International Conference on Computer Vision and Image Analysis Applications, 1– 6. https://doi.org/10.1109/ICCVIA.2015.7351905 Sankur, B., Kahya, Y. P., Guler, E. C., & Engin, T. (n.d.). Feature extraction and classification of nonstationary signals based on the multiresolution signal decomposition. Proceedings of the 12th IAPR International Conference on Pattern Recognition (Cat. No.94CH3440-5), 2, 592–595. https://doi.org/10.1109/ICPR.1994.577049 Saravanan, G., Yamuna, G., & Nandhini, S. (2016). Real time implementation of RGB to HSV/HSI/HSL and its reverse color space models. 2016 International Conference on Communication and Signal Processing (ICCSP), 0462–0466. https://doi.org/10.1109/ICCSP.2016.7754179 Sucar, L. E., & Gómez, G. (2011). Vision Computacional. Instituto Nacional de Astrofísica, Óptica y Electrónica, 185. Tandon, A., Mohan, N., Jensen, C., Burkhardt, B. E. U., Gooty, V., Castellanos, D. A., McKenzie, P. L., Zahr, R. A., Bhattaru, A., Abdulkarim, M., Amir-Khalili, A., Sojoudi, A., Rodriguez, S. M., Dillenbeck, J., Greil, G. F., & Hussain, T. (2021). Retraining Convolutional Neural Networks for Specialized Cardiovascular Imaging Tasks: Lessons from Tetralogy of Fallot. Pediatric Cardiology. https://doi.org/10.1007/s00246-020-02518-5 Tarver, T. (2012). Cancer facts & figures 2012. American cancer society (ACS) Atlanta, GA: American Cancer Society. American Cancer Society, 66. Thrall, J. H., Fessell, D., & Pandharipande, P. V. (2021). Rethinking the Approach to Artificial Intelligence for Medical Image Analysis: The Case for Precision Diagnosis. Journal of the American College of Radiology, 18(1), 174–179. https://doi.org/10.1016/j.jacr.2020.07.010 Tobergte, D. R., & Curtis, S. (2013). Algorithms for image prcessing and computer vision. In Journal of Chemical Information and Modeling (Vol. 53, Issue 9). Trivizakis, E., Ioannidis, G., Melissianos, V., Papadakis, G., Tsatsakis, A., Spandidos, D., & Marias, K. (2019). A novel deep learning architecture outperforming ‘off‑the‑shelf’ transfer learning and feature‑based methods in the automated assessment of mammographic breast density. Oncology Reports. https://doi.org/10.3892/or.2019.7312 van Schie, G., Wallis, M. G., Leifland, K., Danielsson, M., & Karssemeijer, N. (2013). Mass detection in reconstructed digital breast tomosynthesis volumes with a computer-aided detection system trained on 2D mammograms. Medical Physics, 40(4), 041902. https://doi.org/10.1118/1.4791643 Vargas, R., Mosavi, A., & Ruiz, R. (2018). Deep Learning: A Review. Advances in Intelligent Systems and Computing, October. https://doi.org/10.20944/preprints201810.0218.v1 Vedaldi, A., & Fulkerson, B. (2010). Vlfeat. Proceedings of the International Conference on Multimedia - MM ’10, 1469. https://doi.org/10.1145/1873951.1874249 Vilanova, J. C., Comet, J., Garcia-Figueiras, R., Barceló, J., & Boada, M. (2010). Utilidad de la resonancia magnética en el cáncer de próstata. Radiología, 52(6), 513–524. https://doi.org/10.1016/j.rx.2010.06.003 Wang, Y., Zheng, B., Gao, D., & Wang, J. (2018). Fully convolutional neural networks for prostate cancer detection using multi-parametric magnetic resonance images: an initial investigation. 2018 24th International Conference on Pattern Recognition (ICPR), 3814– 3819. https://doi.org/10.1109/ICPR.2018.8545754 Weinreb, J. C., Barentsz, J. O., Choyke, P. L., Cornud, F., Haider, M. A., Macura, K. J., Margolis, D., Schnall, M. D., Shtern, F., Tempany, C. M., Thoeny, H. C., & Verma, S. (2016). PI-RADS Prostate Imaging – Reporting and Data System: 2015, Version 2. European Urology, 69(1), 16–40. https://doi.org/10.1016/j.eururo.2015.08.052 Wen, H., Li, S., Li, W., Li, J., & Yin, C. (2019). Comparision of Four Machine Learning Techniques for the Prediction of Prostate Cancer Survivability. 2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2018, 112–116. https://doi.org/10.1109/ICCWAMTIP.2018.8632577 Wiley, V., & Lucas, T. (2018). Computer Vision and Image Processing: A Paper Review. International Journal of Artificial Intelligence Research, 2(1), 22. https://doi.org/10.29099/ijair.v2i1.42 Yuan, Y., Qin, W., Buyyounouski, M., Ibragimov, B., Hancock, S., Han, B., & Xing, L. (2019). Prostate cancer classification with multiparametric MRI transfer learning model. Medical Physics, 46(2), 756–765. https://doi.org/10.1002/mp.13367 |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| dc.rights.uri.*.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/2.5/co/ |
| dc.rights.local.spa.fl_str_mv |
Abierto (Texto Completo) |
| dc.rights.creativecommons.*.fl_str_mv |
Atribución-NoComercial-SinDerivadas 2.5 Colombia |
| rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-nd/2.5/co/ Abierto (Texto Completo) Atribución-NoComercial-SinDerivadas 2.5 Colombia http://purl.org/coar/access_right/c_abf2 |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.coverage.spatial.spa.fl_str_mv |
Colombia |
| dc.coverage.campus.spa.fl_str_mv |
UNAB Campus Bucaramanga |
| dc.publisher.grantor.spa.fl_str_mv |
Universidad Autónoma de Bucaramanga UNAB |
| dc.publisher.faculty.spa.fl_str_mv |
Facultad Ingeniería |
| dc.publisher.program.spa.fl_str_mv |
Pregrado Ingeniería de Sistemas |
| institution |
Universidad Autónoma de Bucaramanga - UNAB |
| bitstream.url.fl_str_mv |
https://repository.unab.edu.co/bitstream/20.500.12749/15357/1/2021_Tesis_Juan_Felipe_Consuegra.pdf https://repository.unab.edu.co/bitstream/20.500.12749/15357/2/2021_Licencia_Juan_Felipe_Consuegra.pdf https://repository.unab.edu.co/bitstream/20.500.12749/15357/3/license.txt https://repository.unab.edu.co/bitstream/20.500.12749/15357/4/2021_Tesis_Juan_Felipe_Consuegra.pdf.jpg https://repository.unab.edu.co/bitstream/20.500.12749/15357/5/2021_Licencia_Juan_Felipe_Consuegra.pdf.jpg |
| bitstream.checksum.fl_str_mv |
4939fd10d2ca8885d586f5b96a9635e4 ee8ec95e09249c7dc97426dd1fc57de2 3755c0cfdb77e29f2b9125d7a45dd316 6713b79196a47a786c8601e701d89b2d 938ecebfd14450607f2781f58bd26c94 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio Institucional | Universidad Autónoma de Bucaramanga - UNAB |
| repository.mail.fl_str_mv |
repositorio@unab.edu.co |
| _version_ |
1814277351538688000 |
| spelling |
Talero Sarmiento, Leonardo Hernán52f3ced8-d447-4a4d-a30c-74958c9587aaParra Sánchez, Diana Teresab54c5459-8258-4cdc-87a0-aa8ff784c5a1Moreno Corzo, Feisar Enriqueee761f02-1ce9-473f-b811-9b495af86e41Consuegra Rodríguez, Juan Felipeddeb35bb-a97d-40e4-8844-1345dcd2f972Hernández Suárez, Yeison Omar6dbfddc9-d4e2-4283-be03-b4404169e9fdTalero Sarmiento, Leonardo Hernán [0000031387]Moreno Corzo, Feisar Enrique [0001499008]Parra Sánchez, Diana Teresa [0001476224]Moreno Corzo, Feisar Enrique [es&oi=ao]Parra Sánchez, Diana Teresa [es&oi=ao]Talero Sarmiento, Leonardo Hernán [0000-0002-4129-9163]Moreno Corzo, Feisar Enrique [0000-0002-5007-3422]Parra Sánchez, Diana Teresa [0000-0002-7649-0849]Parra Sánchez, Diana Teresa [57195677014]Talero Sarmiento, Leonardo Hernán [Leonardo-Talero]Moreno Corzo, Feisar Enrique [Feisar-Enrique-Moreno-Corzo-2169498891]Parra Sánchez, Diana Teresa [Diana-Parra-Sanchez-2]ColombiaUNAB Campus Bucaramanga2022-01-25T12:51:15Z2022-01-25T12:51:15Z2021-05-18http://hdl.handle.net/20.500.12749/15357instname:Universidad Autónoma de Bucaramanga - UNABreponame:Repositorio Institucional UNABrepourl:https://repository.unab.edu.coEl 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.1. 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 ................................................................................................. 63PregradoProstate 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.Modalidad Presencialapplication/pdfspahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)Atribución-NoComercial-SinDerivadas 2.5 Colombiahttp://purl.org/coar/access_right/c_abf2Prototipo funcional de software para la clasificación de imágenes diagnósticas en el análisis asistido de cáncer de próstata implementando redes neuronales convolucionalesFunctional software prototype for the classification of diagnostic images in the assisted analysis of prostate cancer by implementing convolutional neural networksIngeniero de SistemasUniversidad Autónoma de Bucaramanga UNABFacultad IngenieríaPregrado Ingeniería de Sistemasinfo:eu-repo/semantics/bachelorThesisTrabajo de Gradohttp://purl.org/coar/resource_type/c_7a1fhttp://purl.org/redcol/resource_type/TPSystems engineerTechnological innovationsMachine learningConvolutional neural networkProstate cancerPrototype developmentArtificial intelligenceElectronic data processingSoftware developmentIngeniería de sistemasInnovaciones tecnológicasDesarrollo de prototiposInteligencia artificialProcesamiento electrónico de datosDesarrollo de softwareAprendizaje automáticoRed neuronal convolucionalCáncer de próstataSmall VGG NETInception V3A. R. Syafeeza, M. Khalil-Hani, N. M. Saad, F. Salehuddin, H. N. A. (2015). An Improved Retraining Scheme for Convolutional Neural Network. Journal of Telecommunication, Electronic and Computer Engineering, Vol. 7.Abbasi, A. A., Hussain, L., Awan, I. A., Abbasi, I., Majid, A., Nadeem, M. S. A., & Chaudhary, Q.-A. (2020). Detecting prostate cancer using deep learning convolution neural network with transfer learning approach. Cognitive Neurodynamics, 14(4), 523–533. https://doi.org/10.1007/s11571-020-09587-5Acharya, T., & Ray, A. K. (n.d.). Image Processing Principles and Applications @ Z E i C I E N C E.Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., Van Esesn, B. C., Awwal, A. A. S., & Asari, V. K. (2018). The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches.Alqahtani, S., Wei, C., Zhang, Y., Szewczyk-Bieda, M., Wilson, J., Huang, Z., & Nabi, G. (2020). Prediction of prostate cancer Gleason score upgrading from biopsy to radical prostatectomy using pre-biopsy multiparametric MRI PIRADS scoring system. Scientific Reports, 10(1), 7722. https://doi.org/10.1038/s41598-020-64693-yAmerican Cancer Society. (2017). Prostate Cancer Early Detection , Diagnosis , and Staging Can Ovarian Cancer Be Found Early ? American Cancer Society, 1–25. https://www.cancer.org/cancer/prostate-cancer/detection-diagnosis-staging.htmlAmerican College of Radiology. (2019). PI-RADS v2.1 - ACRAszemi, N. M., & Dominic, P. D. . (2019). Hyperparameter Optimization in Convolutional Neural Network using Genetic Algorithms. International Journal of Advanced Computer Science and Applications, 10(6). https://doi.org/10.14569/IJACSA.2019.0100638Balagourouchetty, L., Pragatheeswaran, J. K., Pottakkat, B., & Ramkumar, G. (2020). GoogLeNet-Based Ensemble FCNet Classifier for Focal Liver Lesion Diagnosis. IEEE Journal of Biomedical and Health Informatics, 24(6), 1686–1694. https://doi.org/10.1109/JBHI.2019.2942774Bosch, A. van den, Hengst, B., Lloyd, J., Miikkulainen, R., Blockeel, H., & Blockeel, H. (2011). Holdout Evaluation. In Encyclopedia of Machine Learning (pp. 506–507). Springer US. https://doi.org/10.1007/978-0-387-30164-8_369Cabaneros, S. M., Calautit, J. K., & Hughes, B. R. (2019). A review of artificial neural network models for ambient air pollution prediction. Environmental Modelling & Software, 119, 285– 304. https://doi.org/10.1016/j.envsoft.2019.06.014Cancelas, J. A., González, R. C., Álvarez, I., & Enguita, J. M. (2016). Procesamiento Morfológico, Visión 3D: Estereoscopía, Álgebra lineal básica para visión por computador, Geometría Proyectiva para Visión 3D. In Conceptos y Métodos en Visión Por Computador (Vol. 1).Cao, R., Mohammadian Bajgiran, A., Afshari Mirak, S., Shakeri, S., Zhong, X., Enzmann, D., Raman, S., & Sung, K. (2019). Joint Prostate Cancer Detection and Gleason Score Prediction in mp-MRI via FocalNet. IEEE Transactions on Medical Imaging, 38(11), 2496– 2506. https://doi.org/10.1109/TMI.2019.2901928Chen, Q., Hu, S., Long, P., Lu, F., Shi, Y., & Li, Y. (2019). A Transfer Learning Approach for Malignant Prostate Lesion Detection on Multiparametric MRI. Technology in Cancer Research & Treatment, 18, 153303381985836. https://doi.org/10.1177/1533033819858363Chen, S., Sun, W., Huang, L., Yang, X., & Huang, J. (2019a). Compressing Fully Connected Layers using Kronecker Tensor Decomposition. Proceedings of IEEE 7th International Conference on Computer Science and Network Technology, ICCSNT 2019, 308–312. https://doi.org/10.1109/ICCSNT47585.2019.8962432Chen, S., Sun, W., Huang, L., Yang, X., & Huang, J. (2019b). Compressing Fully Connected Layers using Kronecker Tensor Decomposition. 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT), 308–312. https://doi.org/10.1109/ICCSNT47585.2019.8962432Cireşan, D., Meier, U., & Schmidhuber, J. (2012). Multi-column Deep Neural Networks for Image Classification. http://arxiv.org/abs/1202.2745Çınar, M., Engin, M., Engin, E. Z., & Ziya Ateşçi, Y. (2009). Early prostate cancer diagnosis by using artificial neural networks and support vector machines. Expert Systems with Applications, 36(3), 6357–6361. https://doi.org/10.1016/j.eswa.2008.08.010Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging, 26(6), 1045–1057. https://doi.org/10.1007/s10278-013-9622-7Colaboratory. (2021). Colaboratory – Google. https://research.google.com/colaboratory/faq.html#resource-limitsCorreia, N. A., Batista, L. T. A., Nascimento, R. J. M., Cangussú, M. C. T., Crugeira, P. J. L., Soares, L. G. P., Silveira, L., & Pinheiro, A. L. B. (2020). Detection of prostate cancer by Raman spectroscopy: A multivariate study on patients with normal and altered PSA values. Journal of Photochemistry and Photobiology B: Biology, 204(January), 111801. https://doi.org/10.1016/j.jphotobiol.2020.111801Costa, D. N., Pedrosa, I., Donato, F., Roehrborn, C. G., & Rofsky, N. M. (2015). MR Imaging– Transrectal US Fusion for Targeted Prostate Biopsies: Implications for Diagnosis and Clinical Management. RadioGraphics, 35(3), 696–708. https://doi.org/10.1148/rg.2015140058Departamento Nacional de Planeación. (2018). BASES DEL PLAN NACIONAL DE DESARROLLO 2018-2022. Pacto Por Colombia, Pacto Por La Equidad, 861. https://www.dnp.gov.co/DNPN/Paginas/Bases-del-Plan-Nacional-deDesarrollo.aspx%0Ahttps://id.presidencia.gov.co/especiales/190523PlanNacionalDesarrollo/documentos/BasesPND2018-2022.pdfFedorov, A., Vangel, M. G., Tempany, C. M., & Fennessy, F. M. (2017). Multiparametric Magnetic Resonance Imaging of the Prostate. Investigative Radiology, 52(9), 538–546. https://doi.org/10.1097/RLI.0000000000000382Fitzmaurice, C., Allen, C., Barber, R. M., Barregard, L., Bhutta, Z. A., Brenner, H., Dicker, D. J., Chimed-Orchir, O., Dandona, R., Dandona, L., Fleming, T., Forouzanfar, M. H., Hancock, J., Hay, R. J., Hunter-Merrill, R., Huynh, C., Hosgood, H. D., Johnson, C. O., Jonas, J. B., … Naghavi, M. (2017). Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-years for 32 Cancer Groups, 1990 to 2015. JAMA Oncology, 3(4), 524. https://doi.org/10.1001/jamaoncol.2016.5688Ghosh, A. (2019). Artificial Intelligence Using Open Source BI-RADS Data Exemplifying Potential Future Use. Journal of the American College of Radiology, 16(1), 64–72. https://doi.org/10.1016/j.jacr.2018.09.040Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep Learning. Cambridge: MIT Press, 1.Guo, Y., Ruan, S., Walker, P., & Feng, Y. (2014). Prostate cancer segmentation from multiparametric MRI based on fuzzy Bayesian model. 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 866–869. https://doi.org/10.1109/ISBI.2014.6868008Jordi Torres. (2018). Deep Learning, Introducción práctica con Keras (PRIMERA PARTE). Kindle Direc.Khan, S., Islam, N., Jan, Z., Ud Din, I., & Rodrigues, J. J. P. C. (2019). A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recognition Letters, 125, 1–6. https://doi.org/10.1016/j.patrec.2019.03.022Kirlik, G., Gullapalli, R., D’Souza, W., Md Daud Iqbal, G., Naslund, M., Wong, J., Papadimitrou, J., Roys, S., Mistry, N., & Zhang, H. (2018). A Supervised Learning Tool for Prostate Cancer Foci Detection and Aggressiveness Identification using Multiparametric magnetic resonance imaging/magnetic resonance spectroscopy imaging. Cancer Informatics, 17, 117693511878626. https://doi.org/10.1177/1176935118786260Kitamura, G., & Deible, C. (2020). Retraining an open-source pneumothorax detecting machine learning algorithm for improved performance to medical images. Clinical Imaging, 61, 15– 19. https://doi.org/10.1016/j.clinimag.2020.01.008Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Neural Information Processing Systems ConferenceKumar, V., Abbas, A. K., & Aster, J. C. (2013). Robbins. Patología Humana (Novena ed). ElsevierLitjens, G., Debats, O., Barentsz, J., Karssemeijer, N., & Huisman, H. (2014). Computer-Aided Detection of Prostate Cancer in MRI. IEEE Transactions on Medical Imaging, 33(5), 1083– 1092. https://doi.org/10.1109/TMI.2014.2303821Liu, S., Zheng, H., Feng, Y., & Li, W. (2017). Prostate cancer diagnosis using deep learning with 3D multiparametric MRI (S. G. Armato & N. A. Petrick (eds.); p. 1013428). https://doi.org/10.1117/12.2277121Liu, Z., Yang, C., Huang, J., Liu, S., Zhuo, Y., & Lu, X. (2021). Deep learning framework based on integration of S-Mask R-CNN and Inception-v3 for ultrasound image-aided diagnosis of prostate cancer. Future Generation Computer Systems, 114, 358–367. https://doi.org/10.1016/j.future.2020.08.015Mesrabadi, H. A., & Faez, K. (2018). Improving early prostate cancer diagnosis by using Artificial Neural Networks and Deep Learning. Proceedings - 2018 4th Iranian Conference of Signal Processing and Intelligent Systems, ICSPIS 2018, 39–42. https://doi.org/10.1109/ICSPIS.2018.8700542Ministerio de Salud y Protección Social. (2018). Observatorio Nacional de Cáncer. ONC Colombia, 1–59Ministerio de Tecnologías de la Información y las Comunicaciones. (2020). Marco de la Transformación Digital para el Estado Colombiano. 86. https://estrategia.gobiernoenlinea.gov.co/623/articles-149178_recurso_1.pdfOkinda, C., Nyalala, I., Korohou, T., Okinda, C., Wang, J., Achieng, T., Wamalwa, P., Mang, T., & Shen, M. (2020). A review on computer vision systems in monitoring of poultry: A welfare perspective. Artificial Intelligence in Agriculture, 4, 184–208. https://doi.org/10.1016/j.aiia.2020.09.002Ordieres, J., Limas, M., Ascacibar, F. J., Alba-Elías, F., González-Marcos, A., Pernía-Espinoza, A., & Vergara, E. (2006). Técnicas y algoritmos básicos de visión artificial Recurso electrónico - En línea (Issue December 2016).Organización de las Naciones Unidas. (2015). Objetivos de Desarrollo Sostenible. https://www.un.org/sustainabledevelopment/es/objetivos-de-desarrollo-sostenible/Parthy, K. (2018). CS231n Convolutional Neural Networks for Visual Recognition. https://cs231n.github.io/convolutional-networks/Perán Teruel, M., Lorenzo-Gómez, M. F., Veiga Canuto, N., Padilla-Fernández, B. Y., ValverdeMartínez, L. S., Migliorini, F., Jorge Pereira, B., Pires Coelho, H. M., & Osca García, J. M. (2020). Complications of transrectal prostate biopsy in our context. International multicenter study of 3350 patients. Actas Urológicas Españolas (English Edition), 44(3), 196–204. https://doi.org/10.1016/j.acuroe.2020.03.002Pressman, R. (2002). Ingeniería del Software. Un enfoque práctico. (McGraw-Hill Interamericana (ed.); Séptima).Ramirez Perez, N. A., Vargas, E. G., & Forero Cuellar, O. M. (2019). Supervised Classifiers of Prostate Cancer from Magnetic Resonance Images in T2 Sequences. 2019 14th Iberian Conference on Information Systems and Technologies (CISTI), 1–4. https://doi.org/10.23919/CISTI.2019.8760647Ray, S. (2019). A Quick Review of Machine Learning Algorithms. 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), 35–39. https://doi.org/10.1109/COMITCon.2019.8862451Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, RealTime Object Detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779–788. https://doi.org/10.1109/CVPR.2016.91Sammouda, R., Aboalsamh, H., & Saeed, F. (2015). Comparison between K mean and fuzzy Cmean methods for segmentation of near infrared fluorescent image for diagnosing prostate cancer. International Conference on Computer Vision and Image Analysis Applications, 1– 6. https://doi.org/10.1109/ICCVIA.2015.7351905Sankur, B., Kahya, Y. P., Guler, E. C., & Engin, T. (n.d.). Feature extraction and classification of nonstationary signals based on the multiresolution signal decomposition. Proceedings of the 12th IAPR International Conference on Pattern Recognition (Cat. No.94CH3440-5), 2, 592–595. https://doi.org/10.1109/ICPR.1994.577049Saravanan, G., Yamuna, G., & Nandhini, S. (2016). Real time implementation of RGB to HSV/HSI/HSL and its reverse color space models. 2016 International Conference on Communication and Signal Processing (ICCSP), 0462–0466. https://doi.org/10.1109/ICCSP.2016.7754179Sucar, L. E., & Gómez, G. (2011). Vision Computacional. Instituto Nacional de Astrofísica, Óptica y Electrónica, 185.Tandon, A., Mohan, N., Jensen, C., Burkhardt, B. E. U., Gooty, V., Castellanos, D. A., McKenzie, P. L., Zahr, R. A., Bhattaru, A., Abdulkarim, M., Amir-Khalili, A., Sojoudi, A., Rodriguez, S. M., Dillenbeck, J., Greil, G. F., & Hussain, T. (2021). Retraining Convolutional Neural Networks for Specialized Cardiovascular Imaging Tasks: Lessons from Tetralogy of Fallot. Pediatric Cardiology. https://doi.org/10.1007/s00246-020-02518-5Tarver, T. (2012). Cancer facts & figures 2012. American cancer society (ACS) Atlanta, GA: American Cancer Society. American Cancer Society, 66.Thrall, J. H., Fessell, D., & Pandharipande, P. V. (2021). Rethinking the Approach to Artificial Intelligence for Medical Image Analysis: The Case for Precision Diagnosis. Journal of the American College of Radiology, 18(1), 174–179. https://doi.org/10.1016/j.jacr.2020.07.010Tobergte, D. R., & Curtis, S. (2013). Algorithms for image prcessing and computer vision. In Journal of Chemical Information and Modeling (Vol. 53, Issue 9).Trivizakis, E., Ioannidis, G., Melissianos, V., Papadakis, G., Tsatsakis, A., Spandidos, D., & Marias, K. (2019). A novel deep learning architecture outperforming ‘off‑the‑shelf’ transfer learning and feature‑based methods in the automated assessment of mammographic breast density. Oncology Reports. https://doi.org/10.3892/or.2019.7312van Schie, G., Wallis, M. G., Leifland, K., Danielsson, M., & Karssemeijer, N. (2013). Mass detection in reconstructed digital breast tomosynthesis volumes with a computer-aided detection system trained on 2D mammograms. Medical Physics, 40(4), 041902. https://doi.org/10.1118/1.4791643Vargas, R., Mosavi, A., & Ruiz, R. (2018). Deep Learning: A Review. Advances in Intelligent Systems and Computing, October. https://doi.org/10.20944/preprints201810.0218.v1Vedaldi, A., & Fulkerson, B. (2010). Vlfeat. Proceedings of the International Conference on Multimedia - MM ’10, 1469. https://doi.org/10.1145/1873951.1874249Vilanova, J. C., Comet, J., Garcia-Figueiras, R., Barceló, J., & Boada, M. (2010). Utilidad de la resonancia magnética en el cáncer de próstata. Radiología, 52(6), 513–524. https://doi.org/10.1016/j.rx.2010.06.003Wang, Y., Zheng, B., Gao, D., & Wang, J. (2018). Fully convolutional neural networks for prostate cancer detection using multi-parametric magnetic resonance images: an initial investigation. 2018 24th International Conference on Pattern Recognition (ICPR), 3814– 3819. https://doi.org/10.1109/ICPR.2018.8545754Weinreb, J. C., Barentsz, J. O., Choyke, P. L., Cornud, F., Haider, M. A., Macura, K. J., Margolis, D., Schnall, M. D., Shtern, F., Tempany, C. M., Thoeny, H. C., & Verma, S. (2016). PI-RADS Prostate Imaging – Reporting and Data System: 2015, Version 2. European Urology, 69(1), 16–40. https://doi.org/10.1016/j.eururo.2015.08.052Wen, H., Li, S., Li, W., Li, J., & Yin, C. (2019). Comparision of Four Machine Learning Techniques for the Prediction of Prostate Cancer Survivability. 2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2018, 112–116. https://doi.org/10.1109/ICCWAMTIP.2018.8632577Wiley, V., & Lucas, T. (2018). Computer Vision and Image Processing: A Paper Review. International Journal of Artificial Intelligence Research, 2(1), 22. https://doi.org/10.29099/ijair.v2i1.42Yuan, Y., Qin, W., Buyyounouski, M., Ibragimov, B., Hancock, S., Han, B., & Xing, L. (2019). Prostate cancer classification with multiparametric MRI transfer learning model. Medical Physics, 46(2), 756–765. https://doi.org/10.1002/mp.13367ORIGINAL2021_Tesis_Juan_Felipe_Consuegra.pdf2021_Tesis_Juan_Felipe_Consuegra.pdfTesisapplication/pdf1268143https://repository.unab.edu.co/bitstream/20.500.12749/15357/1/2021_Tesis_Juan_Felipe_Consuegra.pdf4939fd10d2ca8885d586f5b96a9635e4MD51open access2021_Licencia_Juan_Felipe_Consuegra.pdf2021_Licencia_Juan_Felipe_Consuegra.pdfLicenciaapplication/pdf919059https://repository.unab.edu.co/bitstream/20.500.12749/15357/2/2021_Licencia_Juan_Felipe_Consuegra.pdfee8ec95e09249c7dc97426dd1fc57de2MD52metadata only accessLICENSElicense.txtlicense.txttext/plain; charset=utf-8829https://repository.unab.edu.co/bitstream/20.500.12749/15357/3/license.txt3755c0cfdb77e29f2b9125d7a45dd316MD53open accessTHUMBNAIL2021_Tesis_Juan_Felipe_Consuegra.pdf.jpg2021_Tesis_Juan_Felipe_Consuegra.pdf.jpgIM Thumbnailimage/jpeg5130https://repository.unab.edu.co/bitstream/20.500.12749/15357/4/2021_Tesis_Juan_Felipe_Consuegra.pdf.jpg6713b79196a47a786c8601e701d89b2dMD54open access2021_Licencia_Juan_Felipe_Consuegra.pdf.jpg2021_Licencia_Juan_Felipe_Consuegra.pdf.jpgIM Thumbnailimage/jpeg10064https://repository.unab.edu.co/bitstream/20.500.12749/15357/5/2021_Licencia_Juan_Felipe_Consuegra.pdf.jpg938ecebfd14450607f2781f58bd26c94MD55metadata only access20.500.12749/15357oai:repository.unab.edu.co:20.500.12749/153572023-07-26 16:05:43.277open accessRepositorio Institucional | Universidad Autónoma de Bucaramanga - UNABrepositorio@unab.edu.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 |