Aplicación de técnicas computacionales de inteligencia artificial para el apoyo en el diagnóstico de cáncer de pulmón

Ilustraciones a color, tablas, gráficas

Autores:: Agualimpia Caicedo, Jorge Andrés
Escobar Echeverri, Andrés

Tipo de recurso:: Trabajo de grado de pregrado

Fecha de publicación:: 2024

Institución:: Universidad de San Buenaventura

Repositorio:: Repositorio USB

Idioma:: spa

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dc.title.spa.fl_str_mv	Aplicación de técnicas computacionales de inteligencia artificial para el apoyo en el diagnóstico de cáncer de pulmón
title	Aplicación de técnicas computacionales de inteligencia artificial para el apoyo en el diagnóstico de cáncer de pulmón
spellingShingle	Aplicación de técnicas computacionales de inteligencia artificial para el apoyo en el diagnóstico de cáncer de pulmón Inteligencia artificial -- Aplicaciones Redes neuronales (Computadores) Procesamiento de datos en línea 000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación Tomografía computarizada Cáncer Inteligencia artificial Redes neuronales convolucionales
title_short	Aplicación de técnicas computacionales de inteligencia artificial para el apoyo en el diagnóstico de cáncer de pulmón
title_full	Aplicación de técnicas computacionales de inteligencia artificial para el apoyo en el diagnóstico de cáncer de pulmón
title_fullStr	Aplicación de técnicas computacionales de inteligencia artificial para el apoyo en el diagnóstico de cáncer de pulmón
title_full_unstemmed	Aplicación de técnicas computacionales de inteligencia artificial para el apoyo en el diagnóstico de cáncer de pulmón
title_sort	Aplicación de técnicas computacionales de inteligencia artificial para el apoyo en el diagnóstico de cáncer de pulmón
dc.creator.fl_str_mv	Agualimpia Caicedo, Jorge Andrés Escobar Echeverri, Andrés
dc.contributor.advisor.none.fl_str_mv	Hidalgo Suárez, Carlos Giovanny
dc.contributor.author.none.fl_str_mv	Agualimpia Caicedo, Jorge Andrés Escobar Echeverri, Andrés
dc.contributor.jury.none.fl_str_mv	Paredes Valencia, Carlos Mario Valencia, José Fernando
dc.subject.armarc.none.fl_str_mv	Inteligencia artificial -- Aplicaciones Redes neuronales (Computadores) Procesamiento de datos en línea
topic	Inteligencia artificial -- Aplicaciones Redes neuronales (Computadores) Procesamiento de datos en línea 000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación Tomografía computarizada Cáncer Inteligencia artificial Redes neuronales convolucionales
dc.subject.ddc.none.fl_str_mv	000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación
dc.subject.proposal.spa.fl_str_mv	Tomografía computarizada Cáncer Inteligencia artificial Redes neuronales convolucionales
description	Ilustraciones a color, tablas, gráficas
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-08-09T16:28:41Z
dc.date.available.none.fl_str_mv	2024-08-09T16:28:41Z
dc.date.issued.none.fl_str_mv	2024
dc.type.none.fl_str_mv	Trabajo de grado - Pregrado
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dc.identifier.citation.none.fl_str_mv	Escobar, A, Agualimpia, JA, (2024) Aplicación de técnicas computacionales de inteligencia artificial para el apoyo en el diagnóstico de cáncer de pulmón. Trabajo de grado, Ingeniería de Sistemas, Universidad de San Buenaventura Cali, Facultad de Ingeniería, 2024.
dc.identifier.uri.none.fl_str_mv	https://hdl.handle.net/10819/21352
identifier_str_mv	Escobar, A, Agualimpia, JA, (2024) Aplicación de técnicas computacionales de inteligencia artificial para el apoyo en el diagnóstico de cáncer de pulmón. Trabajo de grado, Ingeniería de Sistemas, Universidad de San Buenaventura Cali, Facultad de Ingeniería, 2024.
url	https://hdl.handle.net/10819/21352
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dc.relation.references.none.fl_str_mv	[1] C. de P. OMS, “OMS \| Cáncer,” WHO, 2017. [2] M. T. Scheuner, T. S. McNeel, and A. N. Freedman, “Population prevalence of familial cancer and common hereditary cancer syndromes. The 2005 California Health Interview Survey,” Genetics in Medicine, vol. 12, no. 11, 2010, doi: 10.1097/GIM.0b013e3181f30e9e. [3] D. Bychkov et al., “Deep learning based tissue analysis predicts outcome in colorectal cancer,” Sci Rep, vol. 8, no. 1, 2018, doi: 10.1038/s41598-018-21758-3. [4] Personal de Mayo Clinic, “Biopsia: Algunos tipos de biopsia que se utilizan para diagnosticar el cáncer - Mayo Clinic,” MAYO CLINIC. Accessed: Aug. 27, 2023. [Online]. Available: https://www.mayoclinic.org/es/diseases-conditions/cancer/in-depth/biopsy/art20043922 [5] J. Gili, “Introduccion biofisica a la Resonancia Magnetica en Neuroimagen,” Books Medicos, vol. 03–2, 2016. [6] Personal de Mayo Clinic, “Resonancia magnética,” MAYO CLINIC. Accessed: Aug. 27, 2023. [Online]. Available: https://www.mayoclinic.org/es/tests-procedures/mri/about/pac20384768 [7] Personal de Mayo Clinic, “Exploración por tomografía computarizada,” MAYO CLINIC. Accessed: Aug. 27, 2023. [Online]. Available: https://www.mayoclinic.org/es/testsprocedures/ct-scan/about/pac-20393675 [8] Personal de Melbourne Radiology Clinic, “What is a Low-Dose CT Scan: LDCT VS Standard CT ,” Melbourne Radiology Clinic. Accessed: Aug. 27, 2023. [Online]. Available: https://www.melbourneradiology.com.au/diagnostic-imaging/what-is-a-low-dose-ct-scan/ [9] H. Sung et al., “Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries,” CA Cancer J Clin, vol. 71, no. 3, 2021, doi: 10.3322/caac.21660. [10] B. H. L. Goulart, M. E. Bensink, D. G. Mummy, and S. D. Ramsey, “Lung cancer screening with low-dose computed tomography: Costs, national expenditures, and cost-effectiveness,” JNCCN Journal of the National Comprehensive Cancer Network, vol. 10, no. 2. 2012. doi: 10.6004/jnccn.2012.0023. [11] G. W. Warren and K. M. Cummings, “Tobacco and Lung Cancer: Risks, Trends, and Outcomes in Patients with Cancer,” American Society of Clinical Oncology Educational Book, no. 33, 2013, doi: 10.14694/edbook_am.2013.33.359. [12] A. F. Cardona et al., “Lung Cancer in Colombia,” Journal of Thoracic Oncology, vol. 17, no. 8. 2022. doi: 10.1016/j.jtho.2022.02.015. [13] H. L. Lancaster, M. A. Heuvelmans, and M. Oudkerk, “Low‐dose computed tomography lung cancer screening: Clinical evidence and implementation research,” J Intern Med, vol. 292, no. 1, pp. 68–80, Jul. 2022, doi: 10.1111/joim.13480. [14] Instituto Nacional de Cancerología ESE, “Información del Cáncer en Colombia ,” Informacion de Cancer en Colombia. Accessed: Aug. 27, 2023. [Online]. Available: https://www.infocancer.co/portal/#!/home [15] J. D. Patel, “ Lo que debe saber sobre el cáncer de pulmón,” Cancer.net. Accessed: Aug. 27, 2023. [Online]. Available: https://www.cancer.net/es/blog/2020-10/lo-que-debe-sabersobre-el-cancer-de-pulmon [16] University of Miami Health System, “¿Cómo se realiza la prueba de detección del cáncer de pulmón?,” University of Miami Health System. Accessed: Aug. 27, 2023. [Online]. Available: https://umiamihealth.org/sylvester-comprehensive-cancer-center/treatmentsand-services/lung-and-chest-cancer/lung-cancer-screening/how-is-lung-cancer-screeningperformed,-q-, [17] M. J. Page et al., “The PRISMA 2020 statement: An updated guideline for reporting systematic reviews,” The BMJ, vol. 372. 2021. doi: 10.1136/bmj.n71. 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Cheng et al., “Recognition of Peripheral Lung Cancer and Focal Pneumonia on Chest Computed Tomography Images Based on Convolutional Neural Network,” Technol Cancer Res Treat, vol. 21, 2022, doi: 10.1177/15330338221085375. [22] M. Bastani et al., “A predictive model for lung cancer screening nonadherence in a community setting health-care network,” JNCI Cancer Spectr, vol. 7, no. 2, 2023, doi: 10.1093/jncics/pkad019. [23] M. T. Lu, V. K. Raghu, T. Mayrhofer, H. J. W. L. Aerts, and U. Hoffmann, “Deep learning using chest radiographs to identify high-risk smokers for lung cancer screening computed tomography: Development and validation of a prediction model,” Ann Intern Med, vol. 173, no. 9, 2020, doi: 10.7326/M20-1868. [24] P. H. Tsou et al., “Exploring volatile organic compounds in breath for high-accuracy prediction of lung cancer,” Cancers (Basel), vol. 13, no. 6, 2021, doi: 10.3390/cancers13061431. [25] S. Agnes and J. Anitha, “Appraisal of deep-learning techniques on computer-aided lung cancer diagnosis with computed tomography screening,” J Med Phys, vol. 45, no. 2, 2020, doi: 10.4103/jmp.JMP_101_19. [26] H. A. H. Chaudhry et al., “UniToChest: A Lung Image Dataset for Segmentation of Cancerous Nodules on CT Scans,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022. doi: 10.1007/978-3-031-06427-2_16. [27] H. Jiang, S. Tang, W. Liu, and Y. Zhang, “Deep learning for COVID-19 chest CT (computed tomography) image analysis: A lesson from lung cancer,” Comput Struct Biotechnol J, vol. 19, 2021, doi: 10.1016/j.csbj.2021.02.016. [28] Y. Pan et al., “Automatic opportunistic osteoporosis screening using low-dose chest computed tomography scans obtained for lung cancer screening,” Eur Radiol, vol. 30, no. 7, 2020, doi: 10.1007/s00330-020-06679-y. [29] V. K. Raghu et al., “Validation of a Deep Learning-Based Model to Predict Lung Cancer Risk Using Chest Radiographs and Electronic Medical Record Data,” JAMA Netw Open, vol. 5, no. 12, 2022, doi: 10.1001/jamanetworkopen.2022.48793. [30] S. J. Yoo et al., “Automated lung segmentation on chest computed tomography images with extensive lung parenchymal abnormalities using a deep neural network,” Korean J Radiol, vol. 22, no. 3, 2021, doi: 10.3348/kjr.2020.0318. [31] K. H. Yu et al., “Reproducible machine learning methods for lung cancer detection using computed tomography images: Algorithm development and validation,” J Med Internet Res, vol. 22, no. 8, 2020, doi: 10.2196/16709. [32] H. Wang, H. Zhu, L. Ding, and K. Yang, “A diagnostic classification of lung nodules using multiple-scale residual network,” Sci Rep, vol. 13, no. 1, 2023, doi: 10.1038/s41598-023-38350-z. [33] J. Juan et al., “Computer-assisted diagnosis for an early identification of lung cancer in chest X rays,” Sci Rep, vol. 13, no. 1, 2023, doi: 10.1038/s41598-023-34835-z. [34] J. Civit-Masot, A. Bañuls-Beaterio, M. Domínguez-Morales, M. Rivas-Pérez, L. MuñozSaavedra, and J. M. Rodríguez Corral, “Non-small cell lung cancer diagnosis aid with histopathological images using Explainable Deep Learning techniques,” Comput Methods Programs Biomed, vol. 226, 2022, doi: 10.1016/j.cmpb.2022.107108. [35] X. Chen, Q. Duan, R. Wu, and Z. Yang, “Segmentation of lung computed tomography images based on SegNet in the diagnosis of lung cancer,” J Radiat Res Appl Sci, vol. 14, no. 1, 2021, doi: 10.1080/16878507.2021.1981753. [36] F. Tohidinezhad et al., “Computed tomography-based radiomics for the differential diagnosis of pneumonitis in stage IV non-small cell lung cancer patients treated with immune checkpoint inhibitors,” Eur J Cancer, vol. 183, 2023, doi: 10.1016/j.ejca.2023.01.027. [37] A. Chon and N. Balachandar, “Deep Convolutional Neural Networks for Lung CancerDetection,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9887 LNCS, no. November, 2016. [38] G. A. Pérez Sarabia, “Automated detection of lung cancer with 3D convolutional neural networks.” Uniandes, 2018. Accessed: Nov. 20, 2023. [Online]. Available: http://hdl.handle.net/1992/34461 [39] M. A. Pfeffer and S. H. Ling, “Evolving Optimised Convolutional Neural Networks for Lung Cancer Classification,” Signals, vol. 3, no. 2, 2022, doi: 10.3390/signals3020018. [40] I. Bonavita, X. Rafael-Palou, M. Ceresa, G. Piella, V. Ribas, and M. A. González Ballester, “Integration of convolutional neural networks for pulmonary nodule malignancy assessment in a lung cancer classification pipeline,” Comput Methods Programs Biomed, vol. 185, 2020, doi: 10.1016/j.cmpb.2019.105172. [41] A. Khan and Z. Ansari, “Identification of Lung Cancer Using Convolutional Neural Networks Based Classification,” 2021. [42] C. Zhang et al., “Toward an Expert Level of Lung Cancer Detection and Classification Using a Deep Convolutional Neural Network,” Oncologist, vol. 24, no. 9, 2019, doi: 10.1634/theoncologist.2018-0908. [43] M. F. Serj, B. Lavi, G. Hoff, and D. P. Valls, “A deep convolutional neural network for lung cancer diagnostic,” arXiv preprint arXiv:1804.08170, 2018. [44] M. Toğaçar, B. Ergen, and Z. Cömert, “Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks,” Biocybern Biomed Eng, vol. 40, no. 1, 2020, doi: 10.1016/j.bbe.2019.11.004. [45] Y. Ieko et al., “Assessment of a computed tomography-based radiomics approach for assessing lung function in lung cancer patients,” Physica Medica, vol. 101, 2022, doi: 10.1016/j.ejmp.2022.07.003. [46] J. H. Kim, H. J. Yoon, E. Lee, I. Kim, Y. K. Cha, and S. H. Bak, “Validation of deep-learning image reconstruction for low-dose chest computed tomography scan: Emphasis on image quality and noise,” Korean J Radiol, vol. 22, no. 1, 2021, doi: 10.3348/kjr.2020.0116. [47] B. AR, “Deep Learning-based Lung Cancer Classification of CT Images using Augmented Convolutional Neural Networks,” ELCVIA Electronic Letters on Computer Vision and Image Analysis, vol. 21, no. 1, Sep. 2022, doi: 10.5565/rev/elcvia.1490. [48] F. Maldonado et al., “Validation of the BRODERS classifier (Benign versus aggressive nodule evaluation using radiomic stratification), a novel HRCT-based radiomic classifier for indeterminate pulmonary nodules,” European Respiratory Journal, vol. 57, no. 4, 2021, doi: 10.1183/13993003.02485-2020. [49] CASEMaker Totem, “What is Rapid Application Development?,” Thesis, 2000. [50] Next.js, “Next.js Documentation,” Next.js Documentation. Accessed: Nov. 18, 2023. [Online]. Available: https://nextjs.org/docs [51] Django, “DjangoThe web framework for perfectionists with deadlines.,” Django. Accessed: Nov. 18, 2023. [Online]. Available: https://www.djangoproject.com/ [52] Django REST Framework, “Django REST Framework,” Django REST Framework. [53] J. Recofsky, “Modelo de Arquitectura ‘4+1,’” Platzi. Accessed: Nov. 20, 2023. [Online]. Available: https://platzi.com/tutoriales/1248-pro-arquitectura/4142-modelo-dearquitectura-41/ [54] C. Pete et al., “Crisp-Dm 1.0,” CRISP-DM Consortium, 2000. [55] Kaggle, “Level up with the largest AI & ML community,” Kaggle. Accessed: Mar. 11, 2024. [Online]. Available: https://www.kaggle.com/ [56] M. Hany, “Chest CT-Scan images Dataset,” Kaggle. Accessed: Mar. 11, 2024. [Online]. Available: https://www.kaggle.com/datasets/mohamedhanyyy/chest-ctscan-images [57] M. Sonka and M. Fitzpatrick, Handbook of Medical Imaging, Volume 2. Medical Image Processing and Analysis. 2010. doi: 10.1117/3.831079. [58] A. Gosthipaty, M. Maynard-Reid, and F. Chollet, “[KerasCV] Image segmentation with a U-Net-like architecture,” Keras. Accessed: Nov. 15, 2023. [Online]. Available: https://keras.io/examples/vision/oxford_pets_image_segmentation/ [59] Google Brain Team, “TensorFlow.” Accessed: Nov. 16, 2023. [Online]. Available: https://www.tensorflow.org/ [60] Microsoft, “Azure Machine Learning.” Accessed: Nov. 16, 2023. [Online]. Available: https://azure.microsoft.com/es-es/products/machine-learning [61] NumPy, “NumPy — NumPy,” NumPy Website. [62] Jupyter, “Jupyter Notebook,” Jupyter. Accessed: Nov. 16, 2023. [Online]. Available: https://docs.jupyter.org/en/latest/ [63] N. Buhl, “F1 Score in Machine Learning,” Encord. Accessed: Mar. 11, 2024. [Online]. Available: https://encord.com/blog/f1-score-in-machine-learning/ [64] T. Fawcett, “An introduction to ROC analysis,” Pattern Recognit Lett, vol. 27, no. 8, 2006, doi: 10.1016/j.patrec.2005.10.010. [65] SAS, “¿Qué es un científico de datos? \| SAS,” SAS. Accessed: Nov. 18, 2023. [Online]. Available: https://www.sas.com/es_co/insights/analytics/what-is-a-data-scientist.html [66] Instituto Nacional del Cancer, “Definición de médico - Diccionario de cáncer del NCI,” Instituto Nacional de Cáncer. Accessed: Nov. 18, 2023. [Online]. Available: https://www.cancer.gov/espanol/publicaciones/diccionarios/diccionario-cancer/def/medico [67] Cambridge University Press & Assessment, “DECISION-MAKER \| significado, definición en el Cambridge English Dictionary,” Cambridge University Press & Assessment. Accessed: Nov. 18, 2023. [Online]. Available: https://dictionary.cambridge.org/esLA/dictionary/english/decision-maker#google_vignette [68] Coursera Staff, “What Is a Machine Learning Engineer? (+ How to Get Started),” Coursera. Accessed: Mar. 11, 2024. [Online]. Available: https://www.coursera.org/articles/what-ismachine-learning-engineer
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spelling	Hidalgo Suárez, Carlos Giovannyvirtual::632-1Agualimpia Caicedo, Jorge Andrés399b61a2-d8e4-4bfd-b1ed-ce42f040c138-1Escobar Echeverri, Andrés37939542-e157-4d20-bb03-5d5f1564f21a-1Paredes Valencia, Carlos Mario1af38be7-81ff-44f5-a276-aac48df6c402600Valencia, José Fernando1398edb9-de4d-4204-b9c0-aa387892e95d-12024-08-09T16:28:41Z2024-08-09T16:28:41Z2024Ilustraciones a color, tablas, gráficasEn el marco de la industria 4.0 y en relación con una de las enfermedades genéticas más mortales del mundo, se propuso como objetivo de este proyecto la creación de un modelo de inteligencia artificial que ayude a detectar y clasificar la presencia de cáncer de pulmón en imágenes diagnósticas. Para esto se indagó en diversos conocimientos de la ciencia de datos que termino llevando a la escogencia de las redes neuronales convolucionales como el algoritmo de deep learning base sobre el cual crear el modelo de inteligencia artificial. A su vez, se planteó el preprocesamiento de los datos; se desarrolló y entrenó el modelo, para su posterior validación, a través de 4 métricas de desempeño distintas. Dichas métricas, permitieron constatar que el modelo con el mejor desempeño obtenido posee un acierto para la predicción de 0.76. También, se desarrollaron dos soluciones de software que permiten que un profesional del sector salud interactúe con el modelo de inteligencia artificial de manera sencilla. Finalmente, el sistema se construyó pensando en que pueda desplegarse en la nube y se probó su despliegue haciendo de los servicios de Microsoft Azure.Within the framework of Industry 4.0 and in relation to one of the most deadly genetic diseases in the world, the objective of this project was the creation of an artificial intelligence model that helps to detect and classify the presence of lung cancer in diagnostic images. For this purpose, various data science knowledges were investigated, which ended up leading to the choice of convolutional neural networks as the base deep learning algorithm on which to create the artificial intelligence model. Also, data preprocessing techniques were applied; the model was developed and trained for its subsequent validation through 4 different performance metrics. These metrics showed that the model with the best performance obtained has a prediction accuracy of 0.76. Continuing, two software solutions were developed that allow a health professional to interact with the artificial intelligence model in a simple way. Finally, the system was built to be deployed in the cloud and its deployment was tested using Microsoft Azure services.PregradoIngeniero de Sistemas96 páginasapplication/pdfEscobar, A, Agualimpia, JA, (2024) Aplicación de técnicas computacionales de inteligencia artificial para el apoyo en el diagnóstico de cáncer de pulmón. Trabajo de grado, Ingeniería de Sistemas, Universidad de San Buenaventura Cali, Facultad de Ingeniería, 2024.https://hdl.handle.net/10819/21352spaUniversidad de San Buenaventura - CaliCaliFacultad de IngenieríaCaliIngeniería de Sistemas[1] C. de P. OMS, “OMS \| Cáncer,” WHO, 2017.[2] M. T. Scheuner, T. S. McNeel, and A. N. Freedman, “Population prevalence of familial cancer and common hereditary cancer syndromes. The 2005 California Health Interview Survey,” Genetics in Medicine, vol. 12, no. 11, 2010, doi: 10.1097/GIM.0b013e3181f30e9e.[3] D. Bychkov et al., “Deep learning based tissue analysis predicts outcome in colorectal cancer,” Sci Rep, vol. 8, no. 1, 2018, doi: 10.1038/s41598-018-21758-3.[4] Personal de Mayo Clinic, “Biopsia: Algunos tipos de biopsia que se utilizan para diagnosticar el cáncer - Mayo Clinic,” MAYO CLINIC. Accessed: Aug. 27, 2023. [Online]. Available: https://www.mayoclinic.org/es/diseases-conditions/cancer/in-depth/biopsy/art20043922[5] J. 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Aplicación de técnicas computacionales de inteligencia artificial para el apoyo en el diagnóstico de cáncer de pulmón

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