Correo Electrónico
DNINFOA - SIA
Bibliotecas
Convocatorias
Identidad U.N.
Published
Detection of COVID-19 and Other Pneumonia Cases Using Convolutional Neural Networks and X-ray Images
Detección de COVID-19 y otros casos de neumonía utilizando redes neuronales convolucionales e imágenes de rayos-X
DOI:
https://doi.org/10.15446/ing.investig.v42n1.90289Keywords:
Coronavirus (COVID-19), Convolutional Neural Networks, Deep Learning, Chest X-Ray images, Pneumonia (en)Coronavirus (COVID-19), redes neuronales convolucionales, aprendizaje profundo, Rayos-X, neumonía (es)
Given that it is fundamental to detect positive COVID-19 cases and treat affected patients quickly to mitigate the impact of the virus, X-ray images have been subjected to research regarding COVID-19, together with deep learning models, eliminating disadvantages such as the scarcity of RT-PCR test kits, their elevated costs, and the long wait for results. The contribution of this paper is to present new models for detecting COVID-19 and other cases of pneumonia using chest X-ray images and convolutional neural networks, thus providing accurate diagnostics in binary and 4-classes classification scenarios. Classification accuracy was improved, and overfitting was prevented by following 2 actions: (1) increasing the data set size while the classification scenarios were balanced; and (2) adding regularization techniques and performing hyperparameter optimization. Additionally, the network capacity and size in the models were reduced as much as possible, making the final models a perfect option to be deployed locally on devices with limited capacities and without the need for Internet access. The impact of key hyperparameters was tested using modern deep learning packages. The final models obtained a classification accuracy of 99,17 and 94,03% for the binary and categorical scenarios, respectively, achieving superior performance compared to other studies in the literature, and requiring a significantly lower number of parameters. The models can also be placed on a digital platform to provide instantaneous diagnostics and surpass the shortage of experts and radiologists.
Dado que es esencial detectar los casos positivos y tratar a los pacientes afectados rápidamente para mitigar el impacto del COVID-19, los rayos-X han sido investigados para la detección del virus, en conjunto con modelos de aprendizaje profundo, eliminando desventajas como la escasez de kits de prueba RT-PCR, sus elevados costos y la larga espera por los resultados. La contribución de este estudio es presentar nuevos modelos para detectar COVID-19 y otros casos de neumonía utilizando imágenes de rayos-X y redes neuronales convolucionales, proporcionando diagnósticos precisos escenarios de clasificación binaria y categórica. La precisión en la clasificación fue mejorada y el sobreajuste fue evitado mediante 2 acciones: (1) aumentando el tamaño del conjunto de datos, al mismo tiempo que los escenarios de clasificación fueron balanceados; y (2) agregando técnicas de regularización y optimizando los hiperparámetros. Adicionalmente, la capacidad y tamaño de los modelos fueron reducidos tanto como fue posible, convirtiendo a los modelos finales en una opción perfecta para ser desplegados localmente en dispositivos con capacidades limitadas y sin necesidad de acceso a Internet. El impacto de hiperparámetros clave fue puesto a prueba utilizando paquetes modernos de aprendizaje profundo. Los modelos finales obtuvieron una precisión de 99,17 y 94,03 % para los escenarios binario y categórico respectivamente, logrando un rendimiento superior en comparación con otras propuestas en la literatura y utilizando un número significativamente menor de parámetros. Los modelos también pueden ser colocados sobre una plataforma digital para proporcionar diagnósticos al instante y superar la escasez de expertos y radiólogos.
References
Abadi, M., Agarwal , A., Barham, P., and Brevdo, E. (2015). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. https://www.tensorflow.org/
American College of Radiology (ACR) (2020, March 11). ACR Recommendations for the use of Chest Radiography and Computed Tomography (CT) for Suspected COVID-19 Infection. https://www.acr.org/Advocacy-and-Economics/ACR-Position-Statements/Recommendations-for-Chest-Radiography-and-CT-for-Suspected-COVID19-Infection
Bergstra, J. (2021). hyperopt PyPI. https://pypi.org/project/hyperopt/
Beysolow II, T. (2017). Introduction to Deep Learning Using R -A Step-by-Step Guide to Lerning and Implementing Deep Learning Models Using R. Apress. DOI: https://doi.org/10.1007/978-1-4842-2734-3
Boccaletti, S., Ditto, W., Mindlin, G., and Atangana, A. (2020). Modeling and forecasting of epidemic spreading: The case of Covid-19 and beyond. Chaos, Solitons and Fractals, 135, 109794. https://doi.org/10.1016/j.chaos.2020.109794
Castillo, O. and Melin, P. (2020). Forecasting of COVID-19 time series for countries in the world based on a hybrid approach combining the fractal dimension and fuzzy logic. Chaos, Solitons and Fractals, 140, 11024. https://doi.org/10.1016/j.chaos.2020.110242
Castillo, O. and Melin, P. (2021). A Novel Method for a COVID-19 Classification of Countries Based on an Intelligent Fuzzy Fractal Approach. Healthcare, 9(2), 196. https://doi.org/10.3390/healthcare9020196
Chaudhary, P. K. and Pachori, R. B. (2020). Automatic diagnosis of COVID-19 and pneumonia using FBD method. In IEEE (Eds.) International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 1-7). IEEE. https://doi.org/10.1109/BIBM49941.2020.9313252
Chen, G., Chen, P., Shi, Y., Hsieh, C.-Y., Liao, B., and Zhang, S. (2019). Rethinking the Usage of Batch Normalization and Dropout in the Training of Deep Neural Networks. arxiv preprint. https://arxiv.org/abs/1905.05928 DOI: https://doi.org/10.29007/3b2l
Chen, X.-W. And Lin, X. (2014). Big Data Deep Learning: Challenges and Perspectives. IEEE Access, 2, 514-525. https://doi.org/10.1109/ACCESS.2014.2325029
Chollet, F. (2015). Keras. https://keras.io
Chollet, F. (2017). Xception: Deep Learning with Depthwise Separable Convolutions. In IEEE (Eds.) Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1800-1807). IEEE. https://doi.org/10.1109/CVPR.2017.195
Chollet, F. (2018). Deep Learning with Python. Manning Publications Co.
Chung, M., Bernheim, A., Mei, X., Zhang, N., Huang, M., Zeng, X., Cui, J., Xu, W., Yang, Y. Fayad, Z. A., Jacobi, A., Li, K., Li, S, and Shang, H. (2020). CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV). Radiology, 295(1), 202-207. https://doi.org/10.1148/radiol.2020200230
Chung, A. (2020a). Actualmed COVID-19 chest x-ray data initiative. https://github.com/agchung/Actualmed-COVID-chestxray-dataset
Chung, A. (2020b). Figure 1 COVID-19 Chest X-Ray Dataset Initiative. https://github.com/agchung/Figure1-COVID-chestxray-dataset
Duchi, J., Hazan, E., and Singer, Y. (2011). Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 12(61), 2121-2159.
Fasihi, M. and Mikhael, W. (2016). Overview of current biomedical image segmentation methods [Conference presentation]. International Conference on Computational Science and Computational Intelligence, Las Vegas, NV, United States of America. https://doi.org/10.1109/CSCI.2016.0156
Gaur, P., Pachori, B. R., Wang, H., and Prasad, G. (2015). An Empirical Mode Decomposition Based Filtering Method for Classification of Motor-Imagery EEG Signals for Enhancing Brain-Computer Interface. In IEEE (Eds.) International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). IEEE. https://doi.org/10.1109/IJCNN.2015.7280754
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep Residual Learning for Image Recognition. In IEEE (Eds.) Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 770-778). IEEE. DOI: https://doi.org/10.1109/CVPR.2016.90
Italian Society of Medical and Interventional Radiology (ISMIR) (2020). COVID-19 DATABASE. https://www.sirm.org/en/category/articles/covid-19-database/
Jacobi, A., Chung, M., Bernheim, A., and Eber, C. (2020). Portable chest X-ray in coronavirus disease-19 (COVID-19): A pictorial review. Clinical Imaging, 64, 35-42. https://doi.org/10.1016/j.clinimag.2020.04.001
James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R. Springer. DOI: https://doi.org/10.1007/978-1-4614-7138-7
Kaggle Inc. (2020). Chest X-Ray Images (Pneumonia). https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia
Kingma, D. and Ba, J. (2014). Adam: a method for stochastic optimization. arXiv preprint. https://arxiv.org/abs/1412.6980v5
Lee, J., Bagheri, B., and Kao, H.-A. (2014). A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18-23. https://doi.org/10.1016/j.mfglet.2014.12.001
Liu, S. and Deng, W. (2015). Very deep convolutional neural network based image classification using small training sample size. In IEEE (Eds.) Proceedings of the 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) (pp. 730-734). IEEE. https://doi.org/10.1109/ACPR.2015.7486599
Melin, P., Monica, J., Sanchez, D., and Castillo, O. (2020). Multiple Ensemble Neural Network Models with Fuzzy Response Aggregation for Predicting COVID-19 Time Series: The Case of Mexico. Healthcare, 8(2), 181. https://doi.org/10.3390/healthcare8020181
Nayak, S. R., Nayak, D. R., Sinha, U., Arora, V., and 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
Nguyen, H., Kieu, L., Wen, T., and Cai, C. (2018). Deep learning methods in transportation domain: a review. IET Intelligent Transport Systems, 12(9), 998-1004. https://doi.org/10.1049/iet-its.2018.0064
Ozturk, T., Talo, M., Yildirim, E., Baloglu, U., and Yildirim, O. (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
Pachori , R., Sharma , R., and Patidar , S. (2015). Classification of Normal and Epileptic Seizure EEG Signals Based on Empirical Mode Decomposition. In Zhu, Q. and Azar, A. (Eds.). Complex System Modelling and Control Through Intelligent Soft Computations (pp. 367-388). Springer. https://doi.org/10.1007/978-3-319-12883-2_13
Patidar, S. and Pachori, R. B. (2013). Segmentation of cardiac sound signals by removing murmurs using constrained tunable-Q wavelet transform. Biomedical Signal Processing and Control, 8(6), 559-567. https://doi.org/10.1016/j.bspc.2013.05.004
Pedamonti, D. (2018). Comparison of non-linear activation functions for deep neural networks on MNIST classification task. arXiv preprint. https://arxiv.org/abs/1804.02763v1
Picciano, A. (2012). The Evolution of Big Data and Learning Analytics in American Higher Education. Journal of Asynchronous Learning Networks, 16(3), 9-20. https://doi.org/10.24059/olj.v16i3.267
Pumperla, M. (2021). hyperas PyPI. https://pypi.org/project/hyperas/
Python Software Foundation. (2020). Python. https://www.python.org/
Raschka, S. and Mirjalili, V. (2017). Python Machine Learning. Packt Publishing.
Rong, G., Mendez, A., Assi, E., Zhao, B., and Sawan, M. (2020). Artificial Intelligence in Healthcare: Review and Prediction Case Studies. Engineering, 6(3), 291-301. https://doi.org/10.1016/j.eng.2019.08.015
Rosebrock, A. (2017). Deep Learning for Computer Vision with Python. PyImageSearch.
SAS Institute Inc. (2018, May 9). Machine Learning: What it is and why it matters | SAS. https://www.sas.com/en_us/insights/analytics/machine-learning.html#machine-learning-importance
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research, 15(56), 1929-1958.
Sun, T. and Wang, Y. (2020). Modeling COVID-19 epidemic in Heilongjiang province, China. Chaos, Solitons and Fractals, 138, 109949. https://doi.org/10.1016/j.chaos.2020.109949
Szegedy, C., Vanhoucke, V., and Ioffe, S. J. (2016). Rethinking the inception architecture for computer vision. In IEEE (Eds.) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2818-2826). IEEE. https://doi.org/10.1109/CVPR.2016.308
Varela-Santos, S. and Melin, P. (2020). A new modular neural network approach with fuzzy response integration for lung disease classification based on multiple objective feature optimization in chest X-ray images. Expert Systems with Applications, 168, 114361. https://doi.org/10.1016/j.eswa.2020.114361
Varela-Santos, S. and Melin, P. (2021). A new approach for classifying coronavirus COVID-19 based on its manifestation on chest X-rays using texture features and neural networks. Information Sciences, 545, 403-414. https://doi.org/10.1016/j.ins.2020.09.041
Wang, J., Ma, Y., Zhang, L., Gao, R., and Wu, D. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems, 48C, 144-156. https://doi.org/10.1016/j.jmsy.2018.01.003
Wang, S.-H., Nayak, D. R., Guttery, D., Zhang, X., and 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
World Health Organization (WHO) (2021, March 30). WHO Coronavirus (COVID-19) Dashboard. https://covid19.who.int/
World Health Organization (WHO) (2020). Coronavirus. https://www.who.int/health-topics/coronavirus#tab=tab_1
Yang, H., Kumara, S., Bukkapatnam, S., and Tsung, F. (2019). The Internet of Things for Smart Manufacturing: A Review. IISE Transactions, 51(11), 1190-1216. https://doi.org/10.1080/24725854.2018.1555383
Zhang, Y., Zhang, X., and Zhu, W. (2021). ANC: Attention Network for COVID-19 Explainable Diagnosis Based on Convolutional Block Attention Module. Computer Modeling in Engineering & Sciences, 127(3), 1037-1058. https://doi.org/10.32604/cmes.2021.015807
Zhou, M., Duan, N., Liu, S., and Shum, H.-Y. (2020). Progress in Neural NLP: Modeling, Learning, and Reasoning. Engineering, 6(3), 275-290. https://doi.org/10.1016/j.eng.2019.12.014
Zhou, S., Wang, Y., Zhu, T., and Xia, L. (2020). CT features of coronavirus disease 2019 (COVID-19). American Journal of Roentgenology, 214(6), 1287-1294. https://doi.org/10.2214/AJR.20.22975
How to Cite
APA
ACM
ACS
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver
Download Citation
CrossRef Cited-by
1. Ronny Stalin Guevara Cruz, Claudio Augusto Delrieux . (2023). Aplicación de redes neuronales densas y convolucionales para detección de COVID_19 en imágenes de rayos X. Revista Conectividad, 4(2), p.19. https://doi.org/10.37431/conectividad.v4i2.78.
2. Nadia L. Quispe Siancas, Jhon E. Monroy Barrios, Wilder Nina Choquehuayta. (2024). Intelligent Systems and Applications. Lecture Notes in Networks and Systems. 1067, p.382. https://doi.org/10.1007/978-3-031-66431-1_26.
3. Qianqian Qi, Shouliang Qi, Yanan Wu, Chen Li, Bin Tian, Shuyue Xia, Jigang Ren, Liming Yang, Hanlin Wang, Hui Yu. (2022). Fully automatic pipeline of convolutional neural networks and capsule networks to distinguish COVID-19 from community-acquired pneumonia via CT images. Computers in Biology and Medicine, 141, p.105182. https://doi.org/10.1016/j.compbiomed.2021.105182.
Dimensions
PlumX
Article abstract page views
Downloads
License
Copyright (c) 2021 Carlos Eduardo Belman López
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.
