Automatic Identification of COVID-19 in Chest X-Ray Images Based on Deep Features and Machine Learning Models

Rubén D. Fonnegra; Fabián R. Narváez; Gloria M. Díaz

doi:10.1007/978-3-030-99170-8_26

Automatic Identification of COVID-19 in Chest X-Ray Images Based on Deep Features and Machine Learning Models

Rubén D. Fonnegra, Fabián R. Narváez, Gloria M. Díaz

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In 2020, the novel coronavirus (COVID-19), spread around the world and became a pandemic. It is diagnosed by a Real-Time Reverse Transcriptase Polymerase Chain reaction (RT-PCR) test, which requires a specialized laboratory to confirm the presence of the virus. Due to the insufficient availability of these labs, medical images have been used as an alternative diagnosis, being the most easily available and least expensive option the Chest X-Ray. As COVID-19 infected patients display very similar respiratory affections like other kinds of pneumonia, distinguish them is difficult even for experienced radiologists. In this paper, two popular deep learning architectures are used to extract deep features, which are then used for training multi-class classification machine learning models to distinguish COVID-19 from healthy, bacterial, and other viral pneumonia infections. The evaluation was performed on a dataset of 7732 images, including 1575 healthy patients, 2801 diagnosed with bacterial pneumonia, 1493 with a viral (no COVID) infection, and 1863 subjects with COVID-19 confirmed diagnosis. The general area under the ROC curve was between 93 % ± 2 % for general categories; and 99 % ± 1 % with a sensitivity of 83 % ± 2 % to identify COVID-19 infected patients.

Original language	English
Title of host publication	Smart Technologies, Systems and Applications - 2nd International Conference, SmartTech-IC 2021, Revised Selected Papers
Editors	Fabián R. Narváez, Julio Proaño, Paulina Morillo, Diego Vallejo, Daniel González Montoya, Gloria M. Díaz
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	360-369
Number of pages	10
ISBN (Print)	9783030991692
DOIs	https://doi.org/10.1007/978-3-030-99170-8_26
State	Published - 2022
Event	2nd International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2021 - Quito, Ecuador Duration: 1 Dec 2021 → 3 Dec 2021

Publication series

Name	Communications in Computer and Information Science
Volume	1532 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	2nd International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2021
Country/Territory	Ecuador
City	Quito
Period	1/12/21 → 3/12/21

Bibliographical note

Funding Information:
This work was supported by the Agencia de Educación Superior de Medellín-Sapiencia, the Universidad Politécnica Salesiana, Ecuador and the Institución Universitaria Pascual Bravo, Colombia and the Instituto Tecnológico Metropolitano, Colombia. We also want to give special thanks to the HM Hospitals, Spain for providing access to the COVID-19 data employed to develop this work.

Funding Information:
Acknowledgements. This work was supported by the Agencia de Educación Superior de Medellín - Sapiencia, the Universidad Politécnica Salesiana, Ecuador and the Institución Universitaria Pascual Bravo, Colombia and the Instituto Tecnológico Metropolitano, Colombia. We also want to give special thanks to the HM Hospitals, Spain for providing access to the COVID-19 data employed to develop this work.

Publisher Copyright:
© 2022, Springer Nature Switzerland AG.

Keywords

COVID-19
Deep learning
Machine learning

Access to Document

10.1007/978-3-030-99170-8_26

Cite this

Fonnegra, R. D., Narváez, F. R., & Díaz, G. M. (2022). Automatic Identification of COVID-19 in Chest X-Ray Images Based on Deep Features and Machine Learning Models. In F. R. Narváez, J. Proaño, P. Morillo, D. Vallejo, D. González Montoya, & G. M. Díaz (Eds.), Smart Technologies, Systems and Applications - 2nd International Conference, SmartTech-IC 2021, Revised Selected Papers (pp. 360-369). (Communications in Computer and Information Science; Vol. 1532 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-99170-8_26

Fonnegra, Rubén D. ; Narváez, Fabián R. ; Díaz, Gloria M. / Automatic Identification of COVID-19 in Chest X-Ray Images Based on Deep Features and Machine Learning Models. Smart Technologies, Systems and Applications - 2nd International Conference, SmartTech-IC 2021, Revised Selected Papers. editor / Fabián R. Narváez ; Julio Proaño ; Paulina Morillo ; Diego Vallejo ; Daniel González Montoya ; Gloria M. Díaz. Springer Science and Business Media Deutschland GmbH, 2022. pp. 360-369 (Communications in Computer and Information Science).

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Fonnegra, RD, Narváez, FR & Díaz, GM 2022, Automatic Identification of COVID-19 in Chest X-Ray Images Based on Deep Features and Machine Learning Models. in FR Narváez, J Proaño, P Morillo, D Vallejo, D González Montoya & GM Díaz (eds), Smart Technologies, Systems and Applications - 2nd International Conference, SmartTech-IC 2021, Revised Selected Papers. Communications in Computer and Information Science, vol. 1532 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 360-369, 2nd International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2021, Quito, Ecuador, 1/12/21. https://doi.org/10.1007/978-3-030-99170-8_26

Automatic Identification of COVID-19 in Chest X-Ray Images Based on Deep Features and Machine Learning Models. / Fonnegra, Rubén D.; Narváez, Fabián R.; Díaz, Gloria M.
Smart Technologies, Systems and Applications - 2nd International Conference, SmartTech-IC 2021, Revised Selected Papers. ed. / Fabián R. Narváez; Julio Proaño; Paulina Morillo; Diego Vallejo; Daniel González Montoya; Gloria M. Díaz. Springer Science and Business Media Deutschland GmbH, 2022. p. 360-369 (Communications in Computer and Information Science; Vol. 1532 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Fonnegra RD, Narváez FR, Díaz GM. Automatic Identification of COVID-19 in Chest X-Ray Images Based on Deep Features and Machine Learning Models. In Narváez FR, Proaño J, Morillo P, Vallejo D, González Montoya D, Díaz GM, editors, Smart Technologies, Systems and Applications - 2nd International Conference, SmartTech-IC 2021, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2022. p. 360-369. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-99170-8_26