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research-article

Improved COVID-19 detection with chest x-ray images using deep learning

Authors:

Jatin Sachdeva,

Senthilkumar Mohan,

Mohd Yazid Bajuri,

Ali AhmadianAuthors Info & Claims

Multimedia Tools and Applications, Volume 81, Issue 26

Pages 37657 - 37680

https://doi.org/10.1007/s11042-022-13509-4

Published: 01 November 2022 Publication History

Abstract

The novel coronavirus disease, which originated in Wuhan, developed into a severe public health problem worldwide. Immense stress in the society and health department was advanced due to the multiplying numbers of COVID carriers and deaths. This stress can be lowered by performing a high-speed diagnosis for the disease, which can be a crucial stride for opposing the deadly virus. A good large amount of time is consumed in the diagnosis. Some applications that use medical images like X-Rays or CT-Scans can pace up the time used in diagnosis. Hence, this paper aims to create a computer-aided-design system that will use the chest X-Ray as input and further classify it into one of the three classes, namely COVID-19, viral Pneumonia, and healthy. Since the COVID-19 positive chest X-Rays dataset was low, we have exploited four pre-trained deep neural networks (DNNs) to find the best for this system. The dataset consisted of 2905 images with 219 COVID-19 cases, 1341 healthy cases, and 1345 viral pneumonia cases. Out of these images, the models were evaluated on 30 images of each class for the testing, while the rest of them were used for training. It is observed that AlexNet attained an accuracy of 97.6% with an average precision, recall, and F1 score of 0.98, 0.97, and 0.98, respectively.

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Cited By

Kosar AAsif MAhmad MAkram WMahmood KKumari S(2024)Towards classification and comprehensive analysis of AI-based COVID-19 diagnostic techniquesArtificial Intelligence in Medicine10.1016/j.artmed.2024.102858151:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.artmed.2024.102858
Koyyada SSingh T(2024)A Systematic Survey of Automatic Detection of Lung Diseases from Chest X-Ray Images: COVID-19, Pneumonia, and TuberculosisSN Computer Science10.1007/s42979-023-02573-85:2Online publication date: 22-Jan-2024
https://dl.acm.org/doi/10.1007/s42979-023-02573-8

Index Terms

Improved COVID-19 detection with chest x-ray images using deep learning
1. Applied computing
  1. Life and medical sciences
    1. Consumer health
    2. Health informatics
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 81, Issue 26

Nov 2022

1364 pages

ISSN:1380-7501

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 November 2022

Accepted: 13 July 2022

Revision received: 18 October 2021

Received: 31 March 2021

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Cited By

Kosar AAsif MAhmad MAkram WMahmood KKumari S(2024)Towards classification and comprehensive analysis of AI-based COVID-19 diagnostic techniquesArtificial Intelligence in Medicine10.1016/j.artmed.2024.102858151:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.artmed.2024.102858
Koyyada SSingh T(2024)A Systematic Survey of Automatic Detection of Lung Diseases from Chest X-Ray Images: COVID-19, Pneumonia, and TuberculosisSN Computer Science10.1007/s42979-023-02573-85:2Online publication date: 22-Jan-2024
https://dl.acm.org/doi/10.1007/s42979-023-02573-8

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