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2022, Number 1

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Revista Cubana de Informática Médica 2022; 14 (1)

Classification of Images of Pneumonia Due to Covid-19 Using Transfer Learning, Based on Convolutional Networks

Preciado RAJ, Flores GFM, Soraluz SAE, Ríos JJG

Language: Spanish
References: 21
Page:
PDF size: 483.48 Kb.


ABSTRACT

Artificial Intelligence has helped to deal with different problems related to massive data in turn to the treatment, diagnosis and detection of diseases such as the one that currently has us in concern, Covid-19. The objective of this research has been to analyze and develop the classification of images of pneumonia due to covid-19 for an effective and optimal diagnosis. Transfer-Learning has been used applying ResNet, DenseNet, Poling and Dense layer for the elaboration of the own network models Covid-Upeu and Covid-UpeU-TL, using Kaggle and Google colab platforms, where 4 experiments have been carried out. The result with a better classification of images was obtained in experiment 4 test N ° 2 with the Covid-UPeU-TL model where Acc.Train: 0.9664 and Acc.Test: 0.9851. The implemented models have been developed with the purpose of having a holistic view of the factors for optimization in the classification of COVID-19 images.


REFERENCES

  1. OMS [Internet]. OMS | Nuevo coronavirus - China. [citado 12 Ene 2020]. Disponible en: https://www.who.int/es/emergencies/diseases/novel-coronavirus-2019

  2. Guan W, Ni Z, Hu Y, Liang W, Ou C, He J, et al. Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med [Internet]. 2020 Apr [cited 2021 Apr 28];382(18):1708–20. Available from: http://www.nejm.org/doi/10.1056/NEJMoa2002032

  3. Adhanom Ghebreyesus T. Alocución de apertura del Director General de la OMS en la rueda de prensa sobre la COVID-19 celebrada el 11 de marzo de 2020 [Internet]. 2020 Mar 11 [citado 17 May 2020]. Disponible en: https://www.who.int/es/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020

  4. Weiss K, Khoshgoftaar TM, Wang DD. A survey of transfer learning. J Big Data [Internet]. 2016 Dec [cited 2020 Jul 8];3(1):[about 40 p.]. Available from https://journalofbigdata.springeropen.com/articles/10.1186/s40537-016-0043-6

  5. Ahuja AS, Reddy VP, Marques O. Artificial intelligence and COVID-19: A multidisciplinary approach. Integr Med Res [Internet]. 2020 Sep [cited 2021 Apr 28];9(3):[about 3 p.]. Available from: https://doi.org/10.1016/j.imr.2020.100434

  6. Fierro AN, Nakano M, Yanai K, Pérez HM. Redes Convolucionales Siamesas y Tripletas para la Recuperación de Imágenes Similares en Contenido. Inf Tecnol [Internet]. 2019 [citado 6 Jul 2020];30(6):243–54. Disponible en: http://dx.doi.org/10.4067/S0718-07642019000600243

  7. Mei X, Lee HC, Diao K yue, Huang M, Lin B, Liu C, et al. Artificial intelligence– enabled rapid diagnosis of patients with COVID-19. Nat Med [Internet]. 2020 Aug [cited 2021 Apr 28];26(8):1224–8. Available from: https://doi.org/10.1038/s41591-020-0931-3

  8. Cortés E, Sanchez S. Deep Learning Transfer with AlexNet for chest X-ray COVID-19 recognition. IEEE Lat Am Trans [Internet]. 2021 Mar [cited 2021 Apr 23];19:944-51. Available from: https://latamt.ieeer9.org/index.php/transactions/article/view/4336

  9. Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Rajendra Acharya U. Automated detection of COVID-19 cases using deep neural networks with Xray images. Comput Biol Med [Internet]. 2020 Jun [cited 2021 Apr 26];121:[about 11 p.]. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0010482520301621

  10. White T. 4 iniciativas que apuestan por la inteligencia artificial y tecnología inclusiva. El Peruano [Internet]. 29 Jul 2019 [citaod 16 May 200]. Disponible en: http://www.elperuano.pe/noticia-tecnologia-inclusiva-83801.aspx

  11. Diaz Marquez J. Inteligencia artificial y Big Data como soluciones frente a la COVID-19. Rev Bioética y Derecho [Internet]. 2020 Nov [citado 28 Abr 2021];315–31. Disponible en: https://scielo.isciii.es/scielo.php?script=sci_arttext&pid=S1886-58872020000300019

  12. Zhou J, Cui G, Zhang Z, Yang C, Liu Z, Wang L, et al. Graph Neural Networks: A Review of Methods and Applications. arXiv [Internet]. 2018 Dec 20 [cited Apr 26];1:57–81. Available from: https://www.sciencedirect.com/science/article/pii/S2666651021000012

  13. Curioso WH, Brunette MJ. Artificial intelligence and innovation to optimize the tuberculosis diagnostic process. Rev Peru Med Exp Salud Publica [Internet]. 2020 Jul [cited 2021 Apr 28];37(3):554–8. Available from: https://doi.org/10.17843/rpmesp.2020.373.5585

  14. Muñoz Herrera W, Bedoya OF, Rincón ME. Aplicación de redes neuronales para la reconstrucción de series de tiempo de precipitación y temperatura utilizando información satelital. Rev EIA [Internet]. 2020 Oct [citado 26 Abr 2021];17(34):[aprox. 16 p.]. Disponible en: https://doi.org/10.24050/reia.v17i34.1292

  15. Quintero C, Merchán F, Cornejo A, Galán JS. Uso de Redes Neuronales Convolucionales para el Reconocimiento Automático de Imágenes de Macroinvertebrados para el Biomonitoreo Participativo. KnE Eng [Internet]. 2018 Feb [citado 26 Abr 2021];3(1):585-96. Disponible en: https://knepublishing.com/index.php/KnEEngineering/article/view/1462/3528

  16. Mooney P. Chest X-Ray Images (Pneumonia) | Kaggle [Internet]. 2018 [cited Apr 27]. Available from: https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia

  17. Praveen. Hack-D-Covid’20 Track-1 [Internet]. 2020 [cited 2021 Apr 27]. Available from: https://www.kaggle.com/c/kjsce-hack-d-covid20-track1

  18. Bodero EM, Lopez MP, Congacha AE, Cajamarca EE, Morales CH. Google Colaboratory como alternativa para el procesamiento de una red neuronal convolucional. ISSN [Internet]. 2020 Mar [citado 27 Apr 2021];41(02):[aprox. 10 p.]. Disponible en: http://www.revistaespacios.com/a20v41n07/a20v41n07p22.pdf

  19. Albo Hernández RO, Guzmán Sánchez MV, Álvarez Díaz I, Bouza Figueroa JF, Calero Ramos R. Requerimientos para mejorar la normalización de datos en software de análisis métricos de la información. Rev. cuba. inf. cienc. salud [Internet]. 2018 Mar [citado 2022 Ene 26] ; 29( 1 ): 55-73. Disponible en: http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S2307-21132018000100005&lng=es.

  20. Bardález-Trigoso G, Bazán-Arzapalo JP, Fabián J, Montenegro-Montori P. (2021). Detección del SARS-CoV-2 en radiografías de tórax por medio de descriptores intermedios y técnicas de machine learning. En Universidad de Lima (Ed.), Construyendo un mundo inteligente para la sostenibilidad . Actas del III Congreso Internacional de Ingeniería de Sistemas (pp. 123-136), Lima, 17 y 20 de noviembre del 2020. Disponible en: https://repositorio.ulima.edu.pe/handle/20.500.12724/13896

  21. Belman López CE. Detection of COVID-19 and Other Pneumonia Cases Using Convolutional Neural Networks and X-ray Images. Ing. Inv. [Internet]. 2022 Jan.1 [cited 2022Jan.26];42(1):e90289. Available from: https://revistas.unal.edu.co/index.php/ingeinv/article/view/90289




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