Correo Electrónico
DNINFOA - SIA
Bibliotecas
Convocatorias
Identidad U.N.
Published
COVID-19 Diagnosis with Deep Learning
Diagnóstico de COVID-19 con Deep Learning
DOI:
https://doi.org/10.15446/ing.investig.v42n1.88825Keywords:
COVID-19, deep learning, convolutional neural network, Zeiler and Fergus network, dense convolutional network-121 (en)COVID-19, deep learning, red neuronal convolucional, red Zeiler y Fergus, red convolucional densa-121 (es)
The coronavirus disease 2019 (COVID-19) is fatal and spreading rapidly. Early detection and diagnosis of the COVID-19 infection will prevent rapid spread. This study aims to automatically detect COVID-19 through a chest computed tomography (CT) dataset. The standard models for automatic COVID-19 detection using raw chest CT images are presented. This study uses convolutional neural network (CNN), Zeiler and Fergus network (ZFNet), and dense convolutional network-121 (DenseNet121) architectures of deep convolutional neural network models. The proposed models are presented to provide accurate diagnosis for binary classification. The datasets were obtained from a public database. This retrospective study included 757 chest CT images (360 confirmed COVID-19 and 397 non-COVID-19 chest CT images). The algorithms were coded using the Python programming language. The performance metrics used were accuracy, precision, recall, F1-score, and ROC-AUC. Comparative analyses are presented between the three models by considering hyper-parameter factors to find the best model. We obtained the best performance, with an accuracy of 94,7%, a recall of 90%, a precision of 100%, and an F1-score of 94,7% from the CNN model. As a result, the CNN algorithm is more accurate and precise than the ZFNet and DenseNet121 models. This study can present a second point of view to medical staff.
La enfermedad del coronavirus 2019 (COVID-19) es fatal y se está propagando rápidamente. La detección y el diagnóstico tempranos de la infección por COVID-19 evitarán la propagación rápida. Este estudio tiene como objetivo detectar COVID-19 automáticamente a partir del conjunto de datos de tomografía computarizada de tórax (TC). Se presentan los modelos estándar para la detección automática de COVID-19 utilizando imágenes de TC de tórax sin procesar. El estudio consta de arquitecturas de red neuronal convolucional (CNN), red Zeiler y Fergus (ZFNet) y red convolucional densa-121 (DenseNet121) de modelos de redes neuronales convolucionales profundas. Los modelos propuestos se presentan para proporcionar diagnósticos precisos para clasificación binaria. Los conjuntos de datos se obtuvieron de una base de datos pública. Este estudio retrospectivo incluyó 757 imágenes de TC de tórax (360 imágenes de TC de tórax COVID-19 confirmadas y 397 imágenes no COVID-19). Los algoritmos se codificaron utilizando el lenguaje de programación Python. Los parámetros de desempeño que se utilizaron fueron exactitud, precisión, recuperación, puntaje-F1 y ROC-AUC. Se presentan análisis comparativos entre los tres modelos considerando factores de hiperparámetros para encontrar el mejor modelo. Obtuvimos el mejor rendimiento, con exactitud del 94,7 %, recuperación del 90 %, precisión del 100 % y puntuación-F1 del 94,7 % del modelo de CNN. Como resultado, el algoritmo de CNN es más exacto y preciso que los modelos ZFNet y DenseNet121. Este estudio puede presentar un segundo punto de vista al personal médico.
References
Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., Tao, Q., Sun, Ziyong, and Xia, L. (2020). Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology, 296(2), E32-E40. https://doi.org/10.1148/radiol.2020200642
Akcay, S., Ozlu, T., and Yılmaz, A. (2020). Radiological approaches to COVID-19 pneumonia. Turkish Journal of Medical Sciences, 50(SI-1), 604-610. https://doi.org/10.3906/sag-2004-160
Ardabili, S., Mosavi, A., Ghamisi, P., Ferdinand, F., Varkonyi-Koczy, A. R., and Reuter, U. R. (2020). COVID-19 outbreak prediction with machine learning. Algorithms, 13(10), 249. https://doi.org/10.3390/a13100249
Ardakani, A. A., Kanafi, A. R., Acharya, U. R., Khadem, N., and Mohammadi, A. (2020). Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: results of 10 convolutional neural networks. Computers in Biology and Medicine, 121, 103795. https://doi.org/10.1016/j.compbiomed.2020.103795
Bao, C., Liu, X., Zhang, H., Li, Y., and Liu, J. (2020). Coronavirus disease 2019 (COVID-19) CT findings: A systematic review and meta-analysis. Journal of the American College of Radiology, 17(6), 701-709. https://doi.org/10.1016/j.jacr.2020.03.006
Chen, X., Wang, Z. J., and McKeown, M. (2016). Joint blind source separation for neurophysiological data analysis: multiset and multimodal methods. IEEE Signal Processing Magazine, 33(3), 86-107. https://doi.org/10.1109/MSP.2016.2521870
Choe, J., Lee, S. M., Do, K-.H., Lee, G., Lee, J-.G., Lee, S. M., and Deo, J. B. (2019). Deep learning–based image conversion of CT reconstruction kernels improves radionics reproducibility for pulmonary nodules or masses. Radiology, 292(2), 365-373. https://doi.org/10.1148/radiol.2019181960
Chung, M., Bernheim, A., Mei, X., Zhang, N., Huang, M., Zeng, X., Cui, J., Yang, Y., Fayad, Z. A., Jacobi, A., Li, K., Li, S., and Shan, H. (2020). CT imaging features of 2019 novel coronavirus (2019-ncov). Radiology, 295, 202-207. https://doi.org/10.1148/radiol.2020200230
Deng, L. (2014). A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Transactions on Signal and Information Processing, 3, 1-29. https://doi.org/10.1017/atsip.2013.9
Esteva, A., Kuprel , B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., and Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542, 115-118. https://doi.org/10.1038/nature21056
GitHub/UCSD-AI4H. (2020, April 20). COVID CT. https://github.com/UCSD-AI4H/COVID-CT
Goreke, V., Sari, V., and Kockanat, S. (2021). A novel classifier architecture based on deep neural network for COVID-19 detection using laboratory findings. Applied Soft Computing Journal, 106, 107329. https://doi.org/10.1016/j.asoc.2021.107329
Guner, R., Hasanoglu, I., and Aktas, F. (2020). COVID-19: Prevention and control measures in community. Turkish Journal of Medical Sciences, 50(S1-1), 571-577. https://doi.org/10.3906/sag-2004-146
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., Cheng, Z., Yu, T., Xia, J., Wei, Y., Wu, W., Xie, X., Yin, W., Li, H., Liu, M. … Cao, B. (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet, 395(10223), 497-506. https://doi.org/10.1016/S0140-6736(20)30183-5
Ioannis, D. A. and Bessiana, B. (2020). COVID-19: Automatic Detection from X-Ray Images Utilizing Transfer Learning with Convolutional Neural Networks. https://arxiv.org/ftp/arxiv/papers/2003/2003.11617.pdf
Jain, R., Gupta, M., Taneja, S., and Hemanth, D. J. (2021). Deep learning based detection and analysis of COVID-19 on chest X-ray images. Applied Intelligence, 51, 1690-1700. https://doi.org/10.1007/s10489-020-01902-1
Jaiswal, A., Gianchandani, N., Singh, D., Kumar, V., and Kaur, M. (2020). Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning. Journal of Biomolecular Structure and Dynamics, 38, 1-8. https://doi.org/10.1080/07391102.2020.1788642
Kamarudin, A. N., Cox, T., and Kolamunnage-Dona, R. (2017). Time-dependent ROC curve analysis in medical research: current methods and applications. BMC Medical Research Methodology, 17(1), 53. https://doi.org/10.1186/s12874-017-0332-6
Lakhani, P. and Sundaram, B. (2017). Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology, 284(2). 574-582. https://doi.org/10.1148/radiol.2017162326
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521, 436-444. https://doi.org/10.1038/nature14539
Lei, J., Li, J., Li, X., and Qi, X. (2020). CT imaging of the 2019 novel coronavirus (2019-ncov) pneumonia. Radiology, 295(1), 18. https://doi.org/10.1148/radiol.2020200236
Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., Bai, J., Lu, Y., Fang, Z., Song, Q., Cao, K., Liu, D., Wang, G., Xu, Q., Fang, X. Zhang, S., Xia, J., and Xia, J. (2020). Artificial intelligence distinguishesCOVID-19 from community acquired pneumonia on chest CT. Radiology, 296(2). https://doi.org/10.1148/radiol.2020200905
Lundervold, A. S. and Lundervold, A. (2019). An overview of deep learning in medical imaging focusing on MRI. Zeitschrift für Medizinische Physik, 29(2), 102-127. https://doi.org/10.1016/j.zemedi.2018.11.002
Narin, A., Kaya, C., and Pamuk, Z. (2020). Automatic Detection of Coronavirus Disease (COVID- 19) Using X-Ray Images and Deep Convolutional Neural Networks. https://arxiv.org/ftp/arxiv/papers/2003/2003.10849.pdf
Ozturk, T., Talo, M., Yildirim, E. A., Baloglu, U. B., Yildirim, O., and Acharya , U. J. (2020). Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine, 121, 103792. https://doi.org/10.1016/j.compbiomed.2020.103792
Panwar, H., Gupta, P. K., Siddiqui, M. K., Morales-Menendez, R., and Singh, V. (2020). Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet. Chaos, Solitons and Fractals, 138, 109944. https://doi.org/10.1016/j.chaos.2020.109944
Pouyanfar, S., Sadiq, S., Yan, Y., Tian, H., Tao, Y., Presa-Reyes, M., Shyu, M-.L., Chen, S-.C., and Iyengar, S. S. (2018). A survey on deep learning: Algorithms, techniques, and applications. ACM Computing Surveys, 51(5), 92. https://doi.org/10.1145/3234150
Sejnowski, T. and Tesauro, G. (1989). The Hebbian rule for synaptic plasticity algorithms and implementations. In J. H. Byme and W. O. Berry (Eds.). Neural models of plasticity (pp. 94-103). California Academic Press. DOI: https://doi.org/10.1016/B978-0-12-148955-7.50010-2
Sriporn, K., Tsai, C. F., Tsai, C. E., and Wang, P. (2020). Analyzing lung disease using highly effective deep learning techniques. Healthcare, 8(107), 1-21. https://doi.org/10.3390/healthcare8020107
Tezer, H. and Bedir Demirdag, T. (2020). Novel coronavirus disease (COVID-19) in children. Turkish Journal of Medical Sciences, 50(SI-1), 592-603. https://doi.org/10.3906/sag-2004-174
Togacar, M., Ergen, B., and Comert, Z. (2020). BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model. Medical Hypotheses, 134, 109531. https://doi.org/10.1016/j.mehy.2019.109531
Wang, L. and Wong, A. (2020). COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images. https://arxiv.org/pdf/2003.09871.pdf DOI: https://doi.org/10.1038/s41598-020-76550-z
World Health Organization (WHO) (2021, April 11). World Health Organization. https://www.who.int/emergencies/diseases/novel-coronavirus-2019
Yang, S., Jiang, L., Cao, Z., Wang, L., Cao, J., Feng, R., Zhang, Z., Xue, X., Shi, Y., and Shan, F. (2020). Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: a pilot study. Annals of Translational Medicine, 8(7), 50. https://doi.org/10.21037/atm.2020.03.132
Zhu, N., Zhang, D., Wang, W. J., Li, X., Yang, B., Song, J., Zhao, X., Huang, B., Shi, W., Lu, R., Niu, P., Zhan, F., Ma, X., Wang, D., Xu, W., Wu, G., Gao, G. F., and Tan, W. (2020). A novel coronavirus from patients with pneumonia in China, 2019. The New England Journal of Medicine, 382(8), 727-733. https://doi.org/10.1056/NEJMoa2001017
How to Cite
APA
ACM
ACS
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver
Download Citation
CrossRef Cited-by
1. Siqi Chen, Tiancheng Li, Luna Yang, Fei Zhai, Xiwei Jiang, Rongwu Xiang, Guixia Ling. (2022). Artificial intelligence-driven prediction of multiple drug interactions. Briefings in Bioinformatics, 23(6) https://doi.org/10.1093/bib/bbac427.
2. Veysel TÜRK, Hatice ÇATAL REİS, Serhat KAYA. (2022). Derin öğrenme mimarilerini kullanarak göğüs BT görüntülerinden otomatik Covid-19 tahmini. Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi, https://doi.org/10.17714/gumusfenbil.1002738.
Dimensions
PlumX
Article abstract page views
Downloads
License
Copyright (c) 2021 Hatice CATAL REIS
This work is licensed under a Creative Commons Attribution 4.0 International License.
The authors or holders of the copyright for each article hereby confer exclusive, limited and free authorization on the Universidad Nacional de Colombia's journal Ingeniería e Investigación concerning the aforementioned article which, once it has been evaluated and approved, will be submitted for publication, in line with the following items:
1. The version which has been corrected according to the evaluators' suggestions will be remitted and it will be made clear whether the aforementioned article is an unedited document regarding which the rights to be authorized are held and total responsibility will be assumed by the authors for the content of the work being submitted to Ingeniería e Investigación, the Universidad Nacional de Colombia and third-parties;
2. The authorization conferred on the journal will come into force from the date on which it is included in the respective volume and issue of Ingeniería e Investigación in the Open Journal Systems and on the journal's main page (https://revistas.unal.edu.co/index.php/ingeinv), as well as in different databases and indices in which the publication is indexed;
3. The authors authorize the Universidad Nacional de Colombia's journal Ingeniería e Investigación to publish the document in whatever required format (printed, digital, electronic or whatsoever known or yet to be discovered form) and authorize Ingeniería e Investigación to include the work in any indices and/or search engines deemed necessary for promoting its diffusion;
4. The authors accept that such authorization is given free of charge and they, therefore, waive any right to receive remuneration from the publication, distribution, public communication and any use whatsoever referred to in the terms of this authorization.
