La Contribución de RESNET34 en la detección de COVID-19, utilizando radiografías de tórax

Contenido principal del artículo

Iván Analuisa Aroca

Resumen

En la lucha contra el avance del COVID-19, las imágenes de radiografías de tórax se convierten en una alternativa en la detección del virus. El interpretar las placas radiográficas para la detección del virus en ocasiones se complica por la presencia de otras enfermedades respiratorias. Se propone como complemento en la detección del virus, una red neuronal convolucional con arquitectura RESNET34 que permiten clasificar las imágenes de radiografías de tórax con COVID-19 e imágenes NOCOVID. El modelo presenta un grado de confiabilidad del 94,78% en la predicción de las imágenes en las dos clases COVID-19 y NOCOVID y un error en la predicción de las imágenes del 5,22%. Los resultados obtenidos, muestran un alto grado de predicción y clasificación de casos de COVID por encima del 90%, demostrando el aporte de la inteligencia artificial en el desarrollo de tecnologías preventivas de enfermedades respiratorias, además de apoyo para el personal médico. 

Descargas

Los datos de descargas todavía no están disponibles.

Detalles del artículo

Cómo citar
Pico Briones, J., Muñoz Muñoz, E., & Analuisa Aroca, I. (2022). La Contribución de RESNET34 en la detección de COVID-19, utilizando radiografías de tórax . INVESTIGATIO, (18), 50–68. https://doi.org/10.31095/investigatio.2022.18.3
Sección
Artículos

Citas

Abbas, A., Abdelsamea, M. M., & Gaber, M. M. (2021). Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Applied Intelligence, 51(2), 854–864. https://doi.org/10.1007/s10489-020-01829-7

Abiyev, R. H., & Ma’aitah, M. K. S. (2018). Deep Convolutional Neural Networks for Chest Diseases Detection. Journal of Healthcare Engineering, 2018. https://doi.org/10.1155/2018/4168538

Ali, K., John, C. N., & Nabeel, M. (2017). AI4COVID-19 : AI Enabled Preliminary Diagnosis for COVID-19 from Cough Samples via an App arXiv : 2004 . 01275v3 [ eess . AS ] 7 Apr 2020. 1–12.

Bai, H. X., Hsieh, B., Xiong, Z., Halsey, K., Choi, J. W., Tran, T. M. L., … Liao, W.-H. (2020). Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT. Radiology, 200823. https://doi.org/10.1148/radiol.2020200823

Bisong, E. (2019). Building Machine Learning and Deep Learning Models on Google Cloud Platform. In Building Machine Learning and Deep Learning Models on Google Cloud Platform (1st ed.). https://doi.org/10.1007/978-1-4842-4470-8

Cohen, J. P., Morrison, P., & Dao, L. (2020). COVID-19 Image Data Collection. Retrieved from http://arxiv.org/abs/2003.11597

Dumoulin, V., Visin, F., & Box, G. E. P. (2018). A guide to convolution arithmetic for deep learning. Retrieved from http://ethanschoonover.com/solarized

Ghoshal, B., & Tucker, A. (2020). Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection. Retrieved from https://www.brunel.ac.uk/computer-science

Goel, T., Murugan, R., Mirjalili, S., & Chakrabartty, D. K. (2021). OptCoNet: an optimized convolutional neural network for an automatic diagnosis of COVID-19. Applied Intelligence, 51(3), 1351–1366. https://doi.org/10.1007/s10489-020-01904-z

Gonzalez, R. C., & Woods, R. E. (2018). Digital Image Processing, 4e (Fourth). Londres: Pearson Education.

Goodfellow, I., Bengio, Y., & Courville, A. (2017). Deep Learning. In The MIT Press. https://doi.org/10.1017/CBO9781107415324.004

Haykin, S. (2009). Neural Networks and Learning Machines (Third). Ontario: Pearson Education.

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-Decem, 770–778. https://doi.org/10.1109/CVPR.2016.90

Howard, J., & Gugger, S. (2020). Deep Learning for Coders with fastai and PyTorch. In O’Reilly Media (First Edit, Vol. 66). Canada.

Huawei Servicios. (2021). Combatiendo al COVID-19 con inteligencia artificial_HUAWEI CLOUD. Retrieved May 27, 2021, from https://www.huaweicloud.com/intl/es-us/cases/covid19.html

Kesim, E., Dokur, Z., & Olmez, T. (2019). X-ray chest image classification by a small-sized convolutional neural network. 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019, 1–5. https://doi.org/10.1109/EBBT.2019.8742050

Khan, A., Shah, J., & Bhat, M. (2020). CoroNet: A Deep Neural Network for Detection and Diagnosis of Covid-19 from Chest X-ray Images.

Kumar, A., Gupta, P. K., & Srivastava, A. (2020). A review of modern technologies for tackling COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 569–573. https://doi.org/https://doi.org/10.1016/j.dsx.2020.05.008

Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539

Lee, K.-S., Kim, J. Y., Jeon, E.-T., Choi, W. S., Kim, N. H., & Lee, K. Y. (2020). Personalized Medicine Evaluation of Scalability and Degree of Fine-Tuning of Deep Convolutional Neural Networks for COVID-19 Screening on Chest X-ray Images Using Explainable Deep-Learning Algorithm. https://doi.org/10.3390/jpm10040213

Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., … Xia, J. (2020). Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on Chest CT. Radiology.

Ly, C., Vachet, C., Schwerdt, I., Abbott, E., Brenkmann, A., McDonald, L. W., & Tasdizen, T. (2020). Determining uranium ore concentrates and their calcination products via image classification of multiple magnifications. Journal of Nuclear Materials, 533. https://doi.org/10.1016/j.jnucmat.2020.152082

Martínez, E., Díez, A., Ibáñez, L., Ossaba, S., & Borruel, S. (2021). Radiologic diagnosis of patients with COVID-19. Radiología (English Edition), 63(1), 56–73. https://doi.org/10.1016/j.rxeng.2020.11.001

Michelucci, U. (2018). Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks. https://doi.org/https://doi.org/10.1007/978-1-4842-3790-8

MSP, M. de S. P. del E. (2021). Actualización de casos de coronavirus en Ecuador. Retrieved from Infografía situacion coronavirus COVID-19 website: https://www.salud.gob.ec/wp-content/uploads/2021/05/INFOGRAFIA-NACIONALCOVID19-COE-NACIONAL-08h00-17052021.pdf

Narin, A., Kaya, C., & Pamuk, Z. (2020, March 24). Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. ArXiv. arXiv.

Nayak, S. R., Nayak, D. R., Sinha, U., Arora, V., & Pachori, R. B. (2021). Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study. Biomedical Signal Processing and Control, 64, 102365. https://doi.org/10.1016/J.BSPC.2020.102365

OMS, O. M. de la S. (2020). SITUATION IN NUMBERS total (new cases in last 24 hours).

Pereira, R. M., Bertolini, D., Teixeira, L. O., Silla, C. N., & Costa, Y. M. G. (2020). COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios. Computer Methods and Programs in Biomedicine, 105532. https://doi.org/https://doi.org/10.1016/j.cmpb.2020.105532

Perry, T. (2020). A Sweat Sensing Patch Aimed at Athletes Takes on COVID-19 - IEEE Spectrum. Retrieved April 10, 2020, from https://spectrum.ieee.org/view-from-the-valley/the-institute/ieee-member-news/sweat-sensing-patch-aimed-athletes-takes-covid19

Rahimzadeh, M., & Attar, A. (2020). A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2. Informatics in Medicine Unlocked, 19, 100360. https://doi.org/10.1016/J.IMU.2020.100360

Salman, F., Abu-Naser, S., Alajrami, E., Abu-Nasser, B., & Ashqar, B. (2020). COVID-19 Detection using Artificial Intelligence. International Journal of Academic Engineering Research (IJAER), 4(3), 18–25.

Santosh, K. C. (2020). AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data. Journal of Medical Systems, 44(5), 93. https://doi.org/10.1007/s10916-020-01562-1

Swapnarekha, H., Behera, H. S., Nayak, J., & Naik, B. (2020). Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review. Chaos, Solitons and Fractals, 138, 109947. https://doi.org/10.1016/j.chaos.2020.109947

Ucar, F., & Korkmaz, D. (2020). COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Medical Hypotheses, 140, 109761. https://doi.org/10.1016/J.MEHY.2020.109761

Vaishya, R., Javaid, M., Khan, I. H., & Haleem, A. (2020). Artificial Intelligence (AI) applications for COVID-19 pandemic. In Diabetes and Metabolic Syndrome: Clinical Research and Reviews (Vol. 14, pp. 337–339). https://doi.org/10.1016/j.dsx.2020.04.012

Vose, A., Balma, J., Heye, A., Rigazzi, A., Siegel, C., Moise, D., … Sukumar, S. R. (2019). Recombination of Artificial Neural Networks.

Waltz, E. (2020). How Do Coronavirus Tests Work? - IEEE Spectrum. Retrieved April 10, 2020, from https://spectrum.ieee.org/the-human-os/biomedical/diagnostics/how-do-coronavirus-tests-work

Wang, L., & Wong, A. (2020). COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images. Retrieved from http://arxiv.org/abs/2003.09871

Wang, S., Nayak, D., Guttery, D., Zhang, X., & Zhang, Y.-D. (2021). COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis. Information Fusion, 68, 131–148. https://doi.org/10.1016/j.inffus.2020.11.005

WHO, W. H. O. (2021). Weekly Operational Update on COVID-19. Retrieved from Update on COVID-19 website: https://www.who.int/publications/m/item/weekly-operational-update-covid-19---17-may-2021

Zhang, J., Xie, Y., Li, Y., Shen, C., & Xia, Y. (2020). COVID-19 Screening on Chest X-ray Images Using Deep Learning based Anomaly Detection.