Prototipo funcional de software para la clasificación de imágenes diagnósticas en el análisis asistido de cáncer de próstata implementando redes neuronales convolucionales

dc.contributorTalero Sarmiento, Leonardo Hernán
dc.contributorParra Sánchez, Diana Teresa
dc.contributorMoreno Corzo, Feisar Enrique
dc.contributorTalero Sarmiento, Leonardo Hernán [0000031387]
dc.contributorMoreno Corzo, Feisar Enrique [0001499008]
dc.contributorParra Sánchez, Diana Teresa [0001476224]
dc.contributorMoreno Corzo, Feisar Enrique [es&oi=ao]
dc.contributorParra Sánchez, Diana Teresa [es&oi=ao]
dc.contributorTalero Sarmiento, Leonardo Hernán [0000-0002-4129-9163]
dc.contributorMoreno Corzo, Feisar Enrique [0000-0002-5007-3422]
dc.contributorParra Sánchez, Diana Teresa [0000-0002-7649-0849]
dc.contributorParra Sánchez, Diana Teresa [57195677014]
dc.contributorTalero Sarmiento, Leonardo Hernán [Leonardo-Talero]
dc.contributorMoreno Corzo, Feisar Enrique [Feisar-Enrique-Moreno-Corzo-2169498891]
dc.contributorParra Sánchez, Diana Teresa [Diana-Parra-Sanchez-2]
dc.creatorConsuegra Rodríguez, Juan Felipe
dc.creatorHernández Suárez, Yeison Omar
dc.date.accessioned2022-01-25T12:51:15Z
dc.date.available2022-01-25T12:51:15Z
dc.date.created2022-01-25T12:51:15Z
dc.date.issued2021-05-18
dc.identifierhttp://hdl.handle.net/20.500.12749/15357
dc.identifierinstname:Universidad Autónoma de Bucaramanga - UNAB
dc.identifierreponame:Repositorio Institucional UNAB
dc.identifierrepourl:https://repository.unab.edu.co
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.
dc.languagespa
dc.publisherUniversidad Autónoma de Bucaramanga UNAB
dc.publisherFacultad Ingeniería
dc.publisherPregrado Ingeniería de Sistemas
dc.relationA. 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.
dc.relationAbbasi, 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
dc.relationAcharya, T., & Ray, A. K. (n.d.). Image Processing Principles and Applications @ Z E i C I E N C E.
dc.relationAlom, 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.
dc.relationAlqahtani, 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
dc.relationAmerican 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
dc.relationAmerican College of Radiology. (2019). PI-RADS v2.1 - ACR
dc.relationAszemi, 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
dc.relationBalagourouchetty, 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
dc.relationBosch, 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
dc.relationCabaneros, 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
dc.relationCancelas, 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).
dc.relationCao, 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
dc.relationChen, 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
dc.relationChen, 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
dc.relationChen, 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
dc.relationCireşan, D., Meier, U., & Schmidhuber, J. (2012). Multi-column Deep Neural Networks for Image Classification. http://arxiv.org/abs/1202.2745
dc.relationÇınar, 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
dc.relationClark, 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
dc.relationColaboratory. (2021). Colaboratory – Google. https://research.google.com/colaboratory/faq.html#resource-limits
dc.relationCorreia, 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
dc.relationCosta, 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
dc.relationDepartamento 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
dc.relationFedorov, 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
dc.relationFitzmaurice, 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
dc.relationGhosh, 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
dc.relationGoodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep Learning. Cambridge: MIT Press, 1.
dc.relationGuo, 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
dc.relationJordi Torres. (2018). Deep Learning, Introducción práctica con Keras (PRIMERA PARTE). Kindle Direc.
dc.relationKhan, 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
dc.relationKirlik, 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
dc.relationKitamura, 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
dc.relationKrizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Neural Information Processing Systems Conference
dc.relationKumar, V., Abbas, A. K., & Aster, J. C. (2013). Robbins. Patología Humana (Novena ed). Elsevier
dc.relationLitjens, 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
dc.relationLiu, 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
dc.relationLiu, 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
dc.relationMesrabadi, 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
dc.relationMinisterio de Salud y Protección Social. (2018). Observatorio Nacional de Cáncer. ONC Colombia, 1–59
dc.relationMinisterio 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
dc.relationOkinda, 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
dc.relationOrdieres, 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).
dc.relationOrganización de las Naciones Unidas. (2015). Objetivos de Desarrollo Sostenible. https://www.un.org/sustainabledevelopment/es/objetivos-de-desarrollo-sostenible/
dc.relationParthy, K. (2018). CS231n Convolutional Neural Networks for Visual Recognition. https://cs231n.github.io/convolutional-networks/
dc.relationPerán 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
dc.relationPressman, R. (2002). Ingeniería del Software. Un enfoque práctico. (McGraw-Hill Interamericana (ed.); Séptima).
dc.relationRamirez 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
dc.relationRay, 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
dc.relationRedmon, 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
dc.relationSammouda, 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
dc.relationSankur, 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
dc.relationSaravanan, 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
dc.relationSucar, L. E., & Gómez, G. (2011). Vision Computacional. Instituto Nacional de Astrofísica, Óptica y Electrónica, 185.
dc.relationTandon, 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
dc.relationTarver, T. (2012). Cancer facts & figures 2012. American cancer society (ACS) Atlanta, GA: American Cancer Society. American Cancer Society, 66.
dc.relationThrall, 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
dc.relationTobergte, D. R., & Curtis, S. (2013). Algorithms for image prcessing and computer vision. In Journal of Chemical Information and Modeling (Vol. 53, Issue 9).
dc.relationTrivizakis, 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
dc.relationvan 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
dc.relationVargas, 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
dc.relationVedaldi, A., & Fulkerson, B. (2010). Vlfeat. Proceedings of the International Conference on Multimedia - MM ’10, 1469. https://doi.org/10.1145/1873951.1874249
dc.relationVilanova, 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
dc.relationWang, 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
dc.relationWeinreb, 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
dc.relationWen, 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
dc.relationWiley, 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
dc.relationYuan, 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
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dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAtribución-NoComercial-SinDerivadas 2.5 Colombia
dc.titlePrototipo funcional de software para la clasificación de imágenes diagnósticas en el análisis asistido de cáncer de próstata implementando redes neuronales convolucionales


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