Machine Learning Screening of COVID-19 Patients Based on X-ray Images for Imbalanced Classes

Ilyes Mrad; Ridha Hamila; Aiman Erbad; Tahir Hamid; Rashid Mazhar; Nasser Al-Emadi

doi:10.1109/EUVIP50544.2021.9484001

Machine Learning Screening of COVID-19 Patients Based on X-ray Images for Imbalanced Classes

Ilyes Mrad, Ridha Hamila, Aiman Erbad, Tahir Hamid, Rashid Mazhar, Nasser Al-Emadi

CSE Information & Computing Technology

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

1 Citation (Scopus)

Abstract

COVID-19 is a virus that has infected more than one hundred and fifty million people and caused more than three million deaths by 13th of Mai 2021 and is having a catastrophic effect on the world population's safety. Therefore, early detection of infected people is essential to fight this pandemic and one of the main screening methods is radiological testing. The goal of this study is the usage of chest x-ray images (CXRs) to effectively identify patients with COVID-19 pneumonia. To achieve an efficient model, we combined three methods named: Convolution Neural Network (CNN), transfer learning, and the focal loss function which is used for imbalanced classes to build 3 binary classifiers, namely COVID-19 vs Normal, COVID-19 vs pneumonia and COVID-19 vs Normal Pneumonia (Normal and Pneumonia). A comparative study has been made between our proposed classifiers with well-known classifiers and provided enhanced results in terms of accuracy, specificity, sensitivity and precision. The high performance of this computer-Aided diagnostic technique may greatly increase the screening speed and reliability of COVID-19 detection.

Original language	English
Title of host publication	Proceedings of the 2021 9th European Workshop on Visual Information Processing, EUVIP 2021
Editors	A. Beghdadi, F. Alaya Cheikh, J.M.R.S. Tavares, A. Mokraoui, G. Valenzise, L. Oudre, M.A. Qureshi
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665432306
DOIs	https://doi.org/10.1109/EUVIP50544.2021.9484001
Publication status	Published - 23 Jun 2021
Event	9th European Workshop on Visual Information Processing, EUVIP 2021 - Paris, France Duration: 23 Jun 2021 → 25 Jun 2021

Publication series

Name	Proceedings - European Workshop on Visual Information Processing, EUVIP
Volume	2021-June
ISSN (Print)	2471-8963

Conference

Conference	9th European Workshop on Visual Information Processing, EUVIP 2021
Country/Territory	France
City	Paris
Period	23/06/21 → 25/06/21

Keywords

COVID-19
chest X-ray images
convolutional neural network
focal loss function

Access to Document

10.1109/EUVIP50544.2021.9484001

Cite this

Mrad, I., Hamila, R., Erbad, A., Hamid, T., Mazhar, R., & Al-Emadi, N. (2021). Machine Learning Screening of COVID-19 Patients Based on X-ray Images for Imbalanced Classes. In A. Beghdadi, F. A. Cheikh, J. M. R. S. Tavares, A. Mokraoui, G. Valenzise, L. Oudre, & M. A. Qureshi (Eds.), Proceedings of the 2021 9th European Workshop on Visual Information Processing, EUVIP 2021 Article 9484001 (Proceedings - European Workshop on Visual Information Processing, EUVIP; Vol. 2021-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EUVIP50544.2021.9484001

Mrad, Ilyes ; Hamila, Ridha ; Erbad, Aiman et al. / Machine Learning Screening of COVID-19 Patients Based on X-ray Images for Imbalanced Classes. Proceedings of the 2021 9th European Workshop on Visual Information Processing, EUVIP 2021. editor / A. Beghdadi ; F. Alaya Cheikh ; J.M.R.S. Tavares ; A. Mokraoui ; G. Valenzise ; L. Oudre ; M.A. Qureshi. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings - European Workshop on Visual Information Processing, EUVIP).

@inproceedings{1633d9ee5e5a4d58b44664d1ac380d39,

title = "Machine Learning Screening of COVID-19 Patients Based on X-ray Images for Imbalanced Classes",

abstract = "COVID-19 is a virus that has infected more than one hundred and fifty million people and caused more than three million deaths by 13th of Mai 2021 and is having a catastrophic effect on the world population's safety. Therefore, early detection of infected people is essential to fight this pandemic and one of the main screening methods is radiological testing. The goal of this study is the usage of chest x-ray images (CXRs) to effectively identify patients with COVID-19 pneumonia. To achieve an efficient model, we combined three methods named: Convolution Neural Network (CNN), transfer learning, and the focal loss function which is used for imbalanced classes to build 3 binary classifiers, namely COVID-19 vs Normal, COVID-19 vs pneumonia and COVID-19 vs Normal Pneumonia (Normal and Pneumonia). A comparative study has been made between our proposed classifiers with well-known classifiers and provided enhanced results in terms of accuracy, specificity, sensitivity and precision. The high performance of this computer-Aided diagnostic technique may greatly increase the screening speed and reliability of COVID-19 detection.",

keywords = "COVID-19, chest X-ray images, convolutional neural network, focal loss function",

author = "Ilyes Mrad and Ridha Hamila and Aiman Erbad and Tahir Hamid and Rashid Mazhar and Nasser Al-Emadi",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 9th European Workshop on Visual Information Processing, EUVIP 2021 ; Conference date: 23-06-2021 Through 25-06-2021",

year = "2021",

month = jun,

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doi = "10.1109/EUVIP50544.2021.9484001",

language = "English",

series = "Proceedings - European Workshop on Visual Information Processing, EUVIP",

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editor = "A. Beghdadi and Cheikh, {F. Alaya} and J.M.R.S. Tavares and A. Mokraoui and G. Valenzise and L. Oudre and M.A. Qureshi",

booktitle = "Proceedings of the 2021 9th European Workshop on Visual Information Processing, EUVIP 2021",

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Mrad, I, Hamila, R, Erbad, A, Hamid, T, Mazhar, R & Al-Emadi, N 2021, Machine Learning Screening of COVID-19 Patients Based on X-ray Images for Imbalanced Classes. in A Beghdadi, FA Cheikh, JMRS Tavares, A Mokraoui, G Valenzise, L Oudre & MA Qureshi (eds), Proceedings of the 2021 9th European Workshop on Visual Information Processing, EUVIP 2021., 9484001, Proceedings - European Workshop on Visual Information Processing, EUVIP, vol. 2021-June, Institute of Electrical and Electronics Engineers Inc., 9th European Workshop on Visual Information Processing, EUVIP 2021, Paris, France, 23/06/21. https://doi.org/10.1109/EUVIP50544.2021.9484001

Machine Learning Screening of COVID-19 Patients Based on X-ray Images for Imbalanced Classes. / Mrad, Ilyes; Hamila, Ridha; Erbad, Aiman et al.
Proceedings of the 2021 9th European Workshop on Visual Information Processing, EUVIP 2021. ed. / A. Beghdadi; F. Alaya Cheikh; J.M.R.S. Tavares; A. Mokraoui; G. Valenzise; L. Oudre; M.A. Qureshi. Institute of Electrical and Electronics Engineers Inc., 2021. 9484001 (Proceedings - European Workshop on Visual Information Processing, EUVIP; Vol. 2021-June).

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

TY - GEN

T1 - Machine Learning Screening of COVID-19 Patients Based on X-ray Images for Imbalanced Classes

AU - Mrad, Ilyes

AU - Hamila, Ridha

AU - Erbad, Aiman

AU - Hamid, Tahir

AU - Mazhar, Rashid

AU - Al-Emadi, Nasser

PY - 2021/6/23

Y1 - 2021/6/23

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AB - COVID-19 is a virus that has infected more than one hundred and fifty million people and caused more than three million deaths by 13th of Mai 2021 and is having a catastrophic effect on the world population's safety. Therefore, early detection of infected people is essential to fight this pandemic and one of the main screening methods is radiological testing. The goal of this study is the usage of chest x-ray images (CXRs) to effectively identify patients with COVID-19 pneumonia. To achieve an efficient model, we combined three methods named: Convolution Neural Network (CNN), transfer learning, and the focal loss function which is used for imbalanced classes to build 3 binary classifiers, namely COVID-19 vs Normal, COVID-19 vs pneumonia and COVID-19 vs Normal Pneumonia (Normal and Pneumonia). A comparative study has been made between our proposed classifiers with well-known classifiers and provided enhanced results in terms of accuracy, specificity, sensitivity and precision. The high performance of this computer-Aided diagnostic technique may greatly increase the screening speed and reliability of COVID-19 detection.

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A2 - Cheikh, F. Alaya

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Mrad I, Hamila R, Erbad A, Hamid T, Mazhar R, Al-Emadi N. Machine Learning Screening of COVID-19 Patients Based on X-ray Images for Imbalanced Classes. In Beghdadi A, Cheikh FA, Tavares JMRS, Mokraoui A, Valenzise G, Oudre L, Qureshi MA, editors, Proceedings of the 2021 9th European Workshop on Visual Information Processing, EUVIP 2021. Institute of Electrical and Electronics Engineers Inc. 2021. 9484001. (Proceedings - European Workshop on Visual Information Processing, EUVIP). doi: 10.1109/EUVIP50544.2021.9484001

Machine Learning Screening of COVID-19 Patients Based on X-ray Images for Imbalanced Classes

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