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A Deep Learning Approach for COVID-19 8 Viral Pneumonia Screening with X-ray Images

Authors:

Syed Ahmad Chan Bukhari,

Fazel KeshtkarAuthors Info & Claims

Digital Government: Research and Practice, Volume 2, Issue 2

Article No.: 18, Pages 1 - 12

https://doi.org/10.1145/3431804

Published: 02 January 2021 Publication History

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Abstract

Beginning in December 2019, the spread of the novel Coronavirus (COVID-19) has exposed weaknesses in healthcare systems across the world. To sufficiently contain the virus, countries have had to carry out a set of extraordinary measures, including exhaustive testing and screening for positive cases of the disease. It is crucial to detect and isolate those who are infected as soon as possible to keep the virus contained. However, in countries and areas where there are limited COVID-19 testing kits, there is an urgent need for alternative diagnostic measures. The standard screening method currently used for detecting COVID-19 cases is RT-PCR testing, which is a very time-consuming, laborious, and complicated manual process. Given that nearly all hospitals have X-ray imaging machines, it is possible to use X-rays to screen for COVID-19 without the dedicated test kits and separate those who are infected and those who are not. In this study, we applied deep convolutional neural networks on chest X-rays to determine this phenomena. The proposed deep learning model produced an average classification accuracy of 90.64% and F1-Score of 89.8% after performing 5-fold cross-validation on a multi-class dataset consisting of COVID-19, Viral Pneumonia, and normal X-ray images.

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Cited By

Khan SAsif SZhao MZou WLi YLi X(2025)Optimized deep learning model for comprehensive medical image analysis across multiple modalitiesNeurocomputing10.1016/j.neucom.2024.129182619(129182)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.129182
Borna MSepehri MMaleki B(2024)An artificial intelligence algorithm to select most viable embryos considering current process in IVF labsFrontiers in Artificial Intelligence10.3389/frai.2024.13754747Online publication date: 30-May-2024
https://doi.org/10.3389/frai.2024.1375474
Chaturvedi ASoni S(2024)Cascaded Deep Learning Model for Detecting Lung Infections Using Chest X-RaysSMART MOVES JOURNAL IJOSCIENCE10.24113/ijoscience.v10i4.498(1-7)Online publication date: 10-Apr-2024
https://doi.org/10.24113/ijoscience.v10i4.498
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Index Terms

A Deep Learning Approach for COVID-19 8 Viral Pneumonia Screening with X-ray Images
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Activity recognition and understanding
  2. Machine learning
    1. Learning settings
      1. Active learning settings

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Information & Contributors

Information

Published In

cover image Digital Government: Research and Practice

Digital Government: Research and Practice Volume 2, Issue 2

COVID-19 Commentaries and Case Study

April 2021

119 pages

EISSN:2639-0175

DOI:10.1145/3442345

Editors:
Soon Ae Chun
City University of New York, USA
,
Beth Simone Noveck
The Governance Lab and New York University, USA

Issue’s Table of Contents

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 January 2021

Online AM: 06 November 2020

Accepted: 01 October 2020

Revised: 01 October 2020

Received: 01 July 2020

Published in DGOV Volume 2, Issue 2

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Cited By

Khan SAsif SZhao MZou WLi YLi X(2025)Optimized deep learning model for comprehensive medical image analysis across multiple modalitiesNeurocomputing10.1016/j.neucom.2024.129182619(129182)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.129182
Borna MSepehri MMaleki B(2024)An artificial intelligence algorithm to select most viable embryos considering current process in IVF labsFrontiers in Artificial Intelligence10.3389/frai.2024.13754747Online publication date: 30-May-2024
https://doi.org/10.3389/frai.2024.1375474
Chaturvedi ASoni S(2024)Cascaded Deep Learning Model for Detecting Lung Infections Using Chest X-RaysSMART MOVES JOURNAL IJOSCIENCE10.24113/ijoscience.v10i4.498(1-7)Online publication date: 10-Apr-2024
https://doi.org/10.24113/ijoscience.v10i4.498
Mohammadian Takaloo VHashemzadeh MGhavidel Neycharan J(2024)DiagCovidPNA: diagnosing and differentiating COVID-19, viral and bacterial pneumonia from chest X-ray images using a hybrid specialized deep learning approachSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-08915-128:15-16(8657-8680)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s00500-023-08915-1
Malik HNaeem ANaqvi RLoh W(2023)DMFL_Net: A Federated Learning-Based Framework for the Classification of COVID-19 from Multiple Chest Diseases Using X-raysSensors10.3390/s2302074323:2(743)Online publication date: 9-Jan-2023
https://doi.org/10.3390/s23020743
Guo KCheng JLi KWang LLv YCao D(2023)Diagnosis and detection of pneumonia using weak-label based on X-ray images: a multi-center studyBMC Medical Imaging10.1186/s12880-023-01174-423:1Online publication date: 12-Dec-2023
https://doi.org/10.1186/s12880-023-01174-4
Kumar SBhowmik B(2023)COVID-19 Waves and Their Impacts to Society2023 IEEE Guwahati Subsection Conference (GCON)10.1109/GCON58516.2023.10183544(1-5)Online publication date: 23-Jun-2023
https://doi.org/10.1109/GCON58516.2023.10183544
Kushal KAhmed TUddin MUddin M(2023)A Blockchain-Based Framework for COVID-19 Detection Using Stacking Ensemble of Pre-Trained ModelsComputer Methods and Programs in Biomedicine Update10.1016/j.cmpbup.2023.1001164(100116)Online publication date: 2023
https://doi.org/10.1016/j.cmpbup.2023.100116
Awassa LJdey IDhahri HHcini GMahmood AOthman EHaneef M(2022)Study of Different Deep Learning Methods for Coronavirus (COVID-19) Pandemic: Taxonomy, Survey and InsightsSensors10.3390/s2205189022:5(1890)Online publication date: 28-Feb-2022
https://doi.org/10.3390/s22051890
Alhaj TIdris IElhaj FElhassan TRemli MSiraj MMohd Rahim M(2022)Preliminary Stages for COVID-19 Detection Using Image ProcessingDiagnostics10.3390/diagnostics1212317112:12(3171)Online publication date: 15-Dec-2022
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