Clasificación de cáncer cervical usando redes neuronales convolucionales, transferencia de aprendizaje y aumento de datos

El cáncer cervical se forma en las células que revisten el cuello uterino y la parte inferior del útero. Debido a razones de costo y baja oferta de servicios destinados a la detección de este tipo de cáncer, muchas mujeres no tienen acceso a un diagnóstico pronto y preciso, ocasionando un inicio tar...

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Autores:: Bravo Ortíz, Mario Alejandro
Arteaga Arteaga, Harold Brayan
Tabares Soto, Reinel
Padilla Buriticá, Jorge Iván
Orozco-Arias, Simon

Tipo de recurso:: Article of journal

Fecha de publicación:: 2020

Institución:: Universidad EIA .

Repositorio:: Repositorio EIA .

Idioma:: spa

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dc.title.spa.fl_str_mv	Clasificación de cáncer cervical usando redes neuronales convolucionales, transferencia de aprendizaje y aumento de datos
dc.title.translated.eng.fl_str_mv	Cervical cancer classification using convolutional neural networks, transfer learning and data augmentation
title	Clasificación de cáncer cervical usando redes neuronales convolucionales, transferencia de aprendizaje y aumento de datos
spellingShingle	Clasificación de cáncer cervical usando redes neuronales convolucionales, transferencia de aprendizaje y aumento de datos Aumento de datos Cáncer cervical Redes neuronales convolucionales Transferencia de aprendizaje data augmentation cervical cancer convolutional neural networks transfer learning
title_short	Clasificación de cáncer cervical usando redes neuronales convolucionales, transferencia de aprendizaje y aumento de datos
title_full	Clasificación de cáncer cervical usando redes neuronales convolucionales, transferencia de aprendizaje y aumento de datos
title_fullStr	Clasificación de cáncer cervical usando redes neuronales convolucionales, transferencia de aprendizaje y aumento de datos
title_full_unstemmed	Clasificación de cáncer cervical usando redes neuronales convolucionales, transferencia de aprendizaje y aumento de datos
title_sort	Clasificación de cáncer cervical usando redes neuronales convolucionales, transferencia de aprendizaje y aumento de datos
dc.creator.fl_str_mv	Bravo Ortíz, Mario Alejandro Arteaga Arteaga, Harold Brayan Tabares Soto, Reinel Padilla Buriticá, Jorge Iván Orozco-Arias, Simon
dc.contributor.author.spa.fl_str_mv	Bravo Ortíz, Mario Alejandro Arteaga Arteaga, Harold Brayan Tabares Soto, Reinel Padilla Buriticá, Jorge Iván Orozco-Arias, Simon
dc.subject.spa.fl_str_mv	Aumento de datos Cáncer cervical Redes neuronales convolucionales Transferencia de aprendizaje
topic	Aumento de datos Cáncer cervical Redes neuronales convolucionales Transferencia de aprendizaje data augmentation cervical cancer convolutional neural networks transfer learning
dc.subject.eng.fl_str_mv	data augmentation cervical cancer convolutional neural networks transfer learning
description	El cáncer cervical se forma en las células que revisten el cuello uterino y la parte inferior del útero. Debido a razones de costo y baja oferta de servicios destinados a la detección de este tipo de cáncer, muchas mujeres no tienen acceso a un diagnóstico pronto y preciso, ocasionando un inicio tardío del tratamiento. Para dar solución a este problema se implementó una metodología que clasifica de manera automática el tipo de cáncer cervical, entre leve (Tipo 1 y 2) y agresivo (Tipo 3), utilizando técnicas de procesamiento digital de imágenes y aprendizaje profundo. Se trabajó en la construcción de un modelo computacional con base en redes neuronales convolucionales, transferencia de aprendizaje y aumento de datos, obteniendo precisiones de clasificación de hasta 97,35% sobre los datos de validación, asegurando la confiabilidad de los resultados. Con este trabajo se demostró que el diseño propuesto puede ser usado como un complemento para mejorar la eficiencia de las herramientas del diagnóstico asistido del cáncer.
publishDate	2020
dc.date.accessioned.none.fl_str_mv	2020-12-31 14:30:36 2022-06-17T20:21:00Z
dc.date.available.none.fl_str_mv	2020-12-31 14:30:36 2022-06-17T20:21:00Z
dc.date.issued.none.fl_str_mv	2020-12-31
dc.type.spa.fl_str_mv	Artículo de revista
dc.type.eng.fl_str_mv	Journal article
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dc.relation.references.spa.fl_str_mv	McGuire S. World cancer report 2014. Geneva, Switzerland: World Health Organization, international agency for research on cancer, WHO Press, 2015. Advances in Nutrition: An International Review Journal, 7, 418-419, 2016. Akshaya R., Manie R., Monisha B., Ranichadra V. Convolutional Neural Networks Aiding Colposcopy Image Classification. International Journal of Trend in Research and development, 5, 270-274, 2018. Almonte M., Sánchez G.I., Jerónimo J., Ferreción C., Lazcano E., Herrera R. Nuevos Paradigmas en la Prevención y Control de Cáncer de Cuello Uterino en América Latina. Salud Pública de México, 52, No 6, 2010. Lorena M., Villate S., Jiménez D., Conduct in regard to the papanicolaou test: The voice of the patients in face of abnormal growth in the cervix, Revista Colombiana de Enfermería, Vol. 18, páginas 1-13, 2019 Kaur N., Panagrahi N., Mittal A. Automated Cervical Cancer Screening Using Transfer Learning. International Journal Of Advanced Research in Science and Engineering, 6, 2110-2119, 2017. Intel & MobileODT, Cervical Cancer Screening, 2017, [Online]. Available: https://www.kaggle.com/c/intel-mobileodt-cervical-cancer-screening/data Simonyan K., Zisserman A. Very Deep Convolutional Networks for Large Scale Image Recognition. Published as a conference paper at ICLR 2015. San Diego, California, Estados Unidos, abril, 2015. Park Chansung, Transfer Learning in Tensorflow (VGG19 on CIFAR-10): Part 1, 2018, 10 Octubre 2019, [Online]. Available: https://towardsdatascience.com/transfer-learning-in-tensorflow-9e4f7eae3bb4 Stanford University, Princeton University, ImageNet, 2016, 10 Octubre 2019, [Online]. Available: http://www.image-net.org/ Zhang XQ, Zhao S-G, Cervical image classification based on image segmentation preprocessing and a CapsNet network model, Wiley, páginas 19-28, 2019 , [Online]. Available: https://doi.org/10.1002/ima.22291 Fernandes K., Cardoso J., Fernandes J., Automated Methods for the Decision Support of Cervical Cancer Screening Using Digital Colposcopies, IEEE Xplore, Vol. 6, páginas 33910-33927, 2018, [Online]. Available: https://ieeexplore.ieee.org/document/8362613 Vasudha, Mittal A., Juneja M., Cervix Cancer Classification using Colposcopy Images by Deep Learning Method, IJETSR, Vol. 5, páginas 426-432, 2018, [Online]. Available: https://pdfs.semanticscholar.org/f099/0cd17037129f7a55fcdf279ea6e9d613e8fe.pdf Caraiman S., Vasile I., Histogram-based segmentation of quantum images, ELSEVIER, Vol. 529, páginas 46-60, 2014, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0304397513005835 Adrian Rosebrock, pyimagesearch, Keras ImageDataGenerator and Data Augmentation(Julio 8, 2019), consultado por última vez el 10 de octubre del 2019 en: https://www.pyimagesearch.com/2019/07/08/keras-imagedatagenerator-and-data-augmentation/?utm_source=facebook&utm_medium=ad-08-07-2019&utm_campaign=8+July+2019+BP+-+Traffic&utm_content=Default+name+-+Traffic&fbid_campaign=6116019415846&fbid_adset=6116019416246&utm_adset=1+July+2019+BP+-+All+Visitors+90+Days+-+Worldwide+-+18%2B&fbid_ad=6116019417246 Mikolajczyk A. Grochowski M, Data augmentation for improving deep learning in image classification problem, IEEE Xplore, Poland, 2018, 21 Junio 2018, [Online]. Available: https://ieeexplore.ieee.org/document/8388338 Intel & MobileODT, Cervical Cancer Screening, 2017, [Online]. Available: https://www.kaggle.com/c/intel-mobileodt-cervical-cancer-screening Tiago S. Nazar´e, Gabriel B. Paranhos da Costa, Welinton A. Contato, and Moacir Ponti, Deep Convolutional Neural Networks and Noisy Images, ResearchGate, paginas 416-424, 2018, [Online]. Available: https://www.researchgate.net/publication/322915518_Deep_Convolutional_Neural_Networks_and_Noisy_Images Nawal M. Nour, Cervical Cancer: A Preventable Death, Obstet Gynecol, Vol. 2, páginas 240-244, 2009, [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2812875/ Ayan E, H. Muray Ü, Data augmentation importance for classification of skin lesions via deep learning, IEEE Xplore, páginas 1-5, 2019, [Online]. Available: https://ieeexplore.ieee.org/document/8391469/citations?tabFilter=papers#citations Keras Documentation, Keras, [Online]. Available: https://keras.io/why-use-keras/ [21]. TensorFlow Core r1.14, Tensorflow, [Online]. Available: https://www.tensorflow.org/versions/r1.14/api_docs/python/tf Krizhevsky A., Sutskever I., Hinton G.E. ImageNet Classification with Deep Convolutional Neural Networks. In NIPS, 2012. Abien Fred M. Agarap, Cornell University, Deep Learning using Rectified Linear Units (ReLU), 2019, 7 febrero 2019, [Online]. Available: https://arxiv.org/abs/1803.08375 Sridhar Narayan, The generalized sigmoid activation function: Competitive supervised learning, ScienceDirect, Vol. 99, páginas 69-82, 1997, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0020025596002009 Daniel Godoy, Towards Data Science, Understanding binary cross-entropy / log loss: a visual explanation, 2018, 10 octubre 2019, [Online]. Available: https://towardsdatascience.com/understanding-binary-cross-entropy-log-loss-a-visual-explanation-a3ac6025181a Zhang S., Choromanska A., and LeCun Y.. Deep learning with Elastic Averaging SGD. Neural Information Processing Systems Conference (NIPS 2015), Vol. 28, páginas 1–24, 2015, [Online]. Available: https://papers.nips.cc/paper/5761-deep-learning-with-elastic-averaging-sgd Piotr Skalski, Towards Data Science, Preventing Deep Neural Network from Overfitting, 2018, 10 Octubre 2019, [Online]. Available: https://towardsdatascience.com/preventing-deep-neural-network-from-overfitting-953458db800a Srivastava N., Hinton G., Krizhevsky A., Sutskever I., Salakhutdinov R., Dropout: A Simple Way to Prevent Neural Networks from Overfitting, Journal of Machine Learning Research, Vol. 15, páginas. 1929-1958, 2014, [Online]. Available: http://jmlr.org/papers/v15/srivastava14a.html
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spelling	Bravo Ortíz, Mario Alejandrofa72add33fa480a7780d9a9dad24e5e4300Arteaga Arteaga, Harold Brayan4a94f99f494e44119ad843f4f82d1fd5300Tabares Soto, Reinelab800effcb910eccdd754c6d1ed7b247300Padilla Buriticá, Jorge Iván3d2983fcca1bc8fa4b3963a07f17a5b5300Orozco-Arias, Simon674d4ca5064e96f4614c2c28d3669bd13002020-12-31 14:30:362022-06-17T20:21:00Z2020-12-31 14:30:362022-06-17T20:21:00Z2020-12-311794-1237https://repository.eia.edu.co/handle/11190/513210.24050/reia.v18i35.14622463-0950https://doi.org/10.24050/reia.v18i35.1462El cáncer cervical se forma en las células que revisten el cuello uterino y la parte inferior del útero. Debido a razones de costo y baja oferta de servicios destinados a la detección de este tipo de cáncer, muchas mujeres no tienen acceso a un diagnóstico pronto y preciso, ocasionando un inicio tardío del tratamiento. Para dar solución a este problema se implementó una metodología que clasifica de manera automática el tipo de cáncer cervical, entre leve (Tipo 1 y 2) y agresivo (Tipo 3), utilizando técnicas de procesamiento digital de imágenes y aprendizaje profundo. Se trabajó en la construcción de un modelo computacional con base en redes neuronales convolucionales, transferencia de aprendizaje y aumento de datos, obteniendo precisiones de clasificación de hasta 97,35% sobre los datos de validación, asegurando la confiabilidad de los resultados. Con este trabajo se demostró que el diseño propuesto puede ser usado como un complemento para mejorar la eficiencia de las herramientas del diagnóstico asistido del cáncer.Cervical cancer is formed in the cells that line the cervix and the lower part of uterus. Due to the cost and low reasons and low supply of services for the detection of this type of cancer many women do not have access to an early an accurate diagnosis. With the purpose of solving this issue ir was created a certain method that helps us to automatically classify the different types of cervical cancer, such as mild type 1 and 2, and aggressive (type 3), using digital image processing techniques and deep learning. We have a built a computational model based on convolutional neural networks, transfer learning and data increase, which help us obtain a classification accuracy up to 97.35% on the validation data, thus, we can ensure the reliability of the results. With this work it was demonstrated that the proposed design can be used as a complement to improve the tools of the assisted diagnosis of cancer.application/pdfspaFondo Editorial EIA - Universidad EIARevista EIA - 2020https://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.http://purl.org/coar/access_right/c_abf2https://revistas.eia.edu.co/index.php/reveia/article/view/1462Aumento de datosCáncer cervicalRedes neuronales convolucionalesTransferencia de aprendizajedata augmentationcervical cancerconvolutional neural networkstransfer learningClasificación de cáncer cervical usando redes neuronales convolucionales, transferencia de aprendizaje y aumento de datosCervical cancer classification using convolutional neural networks, transfer learning and data augmentationArtículo de revistaJournal articlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionTexthttp://purl.org/redcol/resource_type/ARTREFhttp://purl.org/coar/version/c_970fb48d4fbd8a85McGuire S. World cancer report 2014. Geneva, Switzerland: World Health Organization, international agency for research on cancer, WHO Press, 2015. Advances in Nutrition: An International Review Journal, 7, 418-419, 2016.Akshaya R., Manie R., Monisha B., Ranichadra V. Convolutional Neural Networks Aiding Colposcopy Image Classification. International Journal of Trend in Research and development, 5, 270-274, 2018.Almonte M., Sánchez G.I., Jerónimo J., Ferreción C., Lazcano E., Herrera R. Nuevos Paradigmas en la Prevención y Control de Cáncer de Cuello Uterino en América Latina. Salud Pública de México, 52, No 6, 2010.Lorena M., Villate S., Jiménez D., Conduct in regard to the papanicolaou test: The voice of the patients in face of abnormal growth in the cervix, Revista Colombiana de Enfermería, Vol. 18, páginas 1-13, 2019Kaur N., Panagrahi N., Mittal A. Automated Cervical Cancer Screening Using Transfer Learning. International Journal Of Advanced Research in Science and Engineering, 6, 2110-2119, 2017.Intel & MobileODT, Cervical Cancer Screening, 2017, [Online]. Available: https://www.kaggle.com/c/intel-mobileodt-cervical-cancer-screening/dataSimonyan K., Zisserman A. Very Deep Convolutional Networks for Large Scale Image Recognition. Published as a conference paper at ICLR 2015. San Diego, California, Estados Unidos, abril, 2015.Park Chansung, Transfer Learning in Tensorflow (VGG19 on CIFAR-10): Part 1, 2018, 10 Octubre 2019, [Online]. Available: https://towardsdatascience.com/transfer-learning-in-tensorflow-9e4f7eae3bb4Stanford University, Princeton University, ImageNet, 2016, 10 Octubre 2019, [Online]. Available: http://www.image-net.org/Zhang XQ, Zhao S-G, Cervical image classification based on image segmentation preprocessing and a CapsNet network model, Wiley, páginas 19-28, 2019 , [Online]. Available: https://doi.org/10.1002/ima.22291Fernandes K., Cardoso J., Fernandes J., Automated Methods for the Decision Support of Cervical Cancer Screening Using Digital Colposcopies, IEEE Xplore, Vol. 6, páginas 33910-33927, 2018, [Online]. Available: https://ieeexplore.ieee.org/document/8362613Vasudha, Mittal A., Juneja M., Cervix Cancer Classification using Colposcopy Images by Deep Learning Method, IJETSR, Vol. 5, páginas 426-432, 2018, [Online]. Available: https://pdfs.semanticscholar.org/f099/0cd17037129f7a55fcdf279ea6e9d613e8fe.pdfCaraiman S., Vasile I., Histogram-based segmentation of quantum images, ELSEVIER, Vol. 529, páginas 46-60, 2014, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0304397513005835Adrian Rosebrock, pyimagesearch, Keras ImageDataGenerator and Data Augmentation(Julio 8, 2019), consultado por última vez el 10 de octubre del 2019 en: https://www.pyimagesearch.com/2019/07/08/keras-imagedatagenerator-and-data-augmentation/?utm_source=facebook&utm_medium=ad-08-07-2019&utm_campaign=8+July+2019+BP+-+Traffic&utm_content=Default+name+-+Traffic&fbid_campaign=6116019415846&fbid_adset=6116019416246&utm_adset=1+July+2019+BP+-+All+Visitors+90+Days+-+Worldwide+-+18%2B&fbid_ad=6116019417246Mikolajczyk A. Grochowski M, Data augmentation for improving deep learning in image classification problem, IEEE Xplore, Poland, 2018, 21 Junio 2018, [Online]. Available: https://ieeexplore.ieee.org/document/8388338Intel & MobileODT, Cervical Cancer Screening, 2017, [Online]. Available: https://www.kaggle.com/c/intel-mobileodt-cervical-cancer-screeningTiago S. Nazar´e, Gabriel B. Paranhos da Costa, Welinton A. Contato, and Moacir Ponti, Deep Convolutional Neural Networks and Noisy Images, ResearchGate, paginas 416-424, 2018, [Online]. Available: https://www.researchgate.net/publication/322915518_Deep_Convolutional_Neural_Networks_and_Noisy_ImagesNawal M. Nour, Cervical Cancer: A Preventable Death, Obstet Gynecol, Vol. 2, páginas 240-244, 2009, [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2812875/Ayan E, H. Muray Ü, Data augmentation importance for classification of skin lesions via deep learning, IEEE Xplore, páginas 1-5, 2019, [Online]. Available: https://ieeexplore.ieee.org/document/8391469/citations?tabFilter=papers#citationsKeras Documentation, Keras, [Online]. Available: https://keras.io/why-use-keras/ [21]. TensorFlow Core r1.14, Tensorflow, [Online]. Available: https://www.tensorflow.org/versions/r1.14/api_docs/python/tfKrizhevsky A., Sutskever I., Hinton G.E. ImageNet Classification with Deep Convolutional Neural Networks. In NIPS, 2012.Abien Fred M. Agarap, Cornell University, Deep Learning using Rectified Linear Units (ReLU), 2019, 7 febrero 2019, [Online]. Available: https://arxiv.org/abs/1803.08375Sridhar Narayan, The generalized sigmoid activation function: Competitive supervised learning, ScienceDirect, Vol. 99, páginas 69-82, 1997, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0020025596002009Daniel Godoy, Towards Data Science, Understanding binary cross-entropy / log loss: a visual explanation, 2018, 10 octubre 2019, [Online]. Available: https://towardsdatascience.com/understanding-binary-cross-entropy-log-loss-a-visual-explanation-a3ac6025181aZhang S., Choromanska A., and LeCun Y.. Deep learning with Elastic Averaging SGD. Neural Information Processing Systems Conference (NIPS 2015), Vol. 28, páginas 1–24, 2015, [Online]. Available: https://papers.nips.cc/paper/5761-deep-learning-with-elastic-averaging-sgdPiotr Skalski, Towards Data Science, Preventing Deep Neural Network from Overfitting, 2018, 10 Octubre 2019, [Online]. Available: https://towardsdatascience.com/preventing-deep-neural-network-from-overfitting-953458db800aSrivastava N., Hinton G., Krizhevsky A., Sutskever I., Salakhutdinov R., Dropout: A Simple Way to Prevent Neural Networks from Overfitting, Journal of Machine Learning Research, Vol. 15, páginas. 1929-1958, 2014, [Online]. Available: http://jmlr.org/papers/v15/srivastava14a.htmlhttps://revistas.eia.edu.co/index.php/reveia/article/download/1462/1391Núm. 35 , Año 2021123535008 pp. 118Revista EIAPublicationOREORE.xmltext/xml2846https://repository.eia.edu.co/bitstreams/8368466e-1ed6-457c-b80a-3053aaa20700/downloadcdd2cc2f8d00c6211946bff4aef16fecMD5111190/5132oai:repository.eia.edu.co:11190/51322023-07-25 17:06:28.18https://creativecommons.org/licenses/by-nc-nd/4.0Revista EIA - 2020metadata.onlyhttps://repository.eia.edu.coRepositorio Institucional Universidad EIAbdigital@metabiblioteca.com

Clasificación de cáncer cervical usando redes neuronales convolucionales, transferencia de aprendizaje y aumento de datos

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