Volume 39, Issue 3 e12749

ORIGINAL ARTICLE

Automated COVID-19 detection in chest X-ray images using fine-tuned deep learning architectures

Sonam Aggarwal,

Sonam Aggarwal

orcid.org/0000-0003-2078-2145

Chitkara University Institute of Engineering and Technology, Chitkara University, Chandigarh, Punjab, India

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Sheifali Gupta,

Sheifali Gupta

Chitkara University Institute of Engineering and Technology, Chitkara University, Chandigarh, Punjab, India

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Adi Alhudhaif,

Corresponding Author

Adi Alhudhaif

[email protected]

orcid.org/0000-0002-7201-6963

Department of Computer Science, College of Computer Engineering and Sciences in Al-kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia

Correspondence

Adi Alhudhaif, Department of Computer Science, College of Computer Engineering and Sciences in Al-kharj, Prince Sattam bin Abdulaziz University, P.O. Box 151, Al-Kharj 11942, Saudi Arabia.

Email: [email protected]

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Deepika Koundal,

Deepika Koundal

Department of Virtualization, School of Computer Science, Dehradun, Uttarakhand, India

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Rupesh Gupta,

Rupesh Gupta

Chitkara University Institute of Engineering and Technology, Chitkara University, Chandigarh, Punjab, India

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Kemal Polat,

Kemal Polat

orcid.org/0000-0003-1840-9958

Department of Electrical and Electronics Engineering, Faculty of Engineering, Bolu Abant Izzet Baysal University, Bolu, Turkey

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Sonam Aggarwal,

Sonam Aggarwal

orcid.org/0000-0003-2078-2145

Chitkara University Institute of Engineering and Technology, Chitkara University, Chandigarh, Punjab, India

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Sheifali Gupta,

Sheifali Gupta

Chitkara University Institute of Engineering and Technology, Chitkara University, Chandigarh, Punjab, India

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Adi Alhudhaif,

Corresponding Author

Adi Alhudhaif

[email protected]

orcid.org/0000-0002-7201-6963

Department of Computer Science, College of Computer Engineering and Sciences in Al-kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia

Correspondence

Adi Alhudhaif, Department of Computer Science, College of Computer Engineering and Sciences in Al-kharj, Prince Sattam bin Abdulaziz University, P.O. Box 151, Al-Kharj 11942, Saudi Arabia.

Email: [email protected]

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Deepika Koundal,

Deepika Koundal

Department of Virtualization, School of Computer Science, Dehradun, Uttarakhand, India

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Rupesh Gupta,

Rupesh Gupta

Chitkara University Institute of Engineering and Technology, Chitkara University, Chandigarh, Punjab, India

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Kemal Polat,

Kemal Polat

orcid.org/0000-0003-1840-9958

Department of Electrical and Electronics Engineering, Faculty of Engineering, Bolu Abant Izzet Baysal University, Bolu, Turkey

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First published: 10 June 2021

https://doi.org/10.1111/exsy.12749

Citations: 20

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Abstract

The COVID-19 pandemic has a significant impact on human health globally. The illness is due to the presence of a virus manifesting itself in a widespread disease resulting in a high mortality rate in the whole world. According to the study, infected patients have distinct radiographic visual characteristics as well as dry cough, breathlessness, fever, and other symptoms. Although, the reverse transcription polymerase-chain reaction (RT-PCR) test has been used for COVID-19 testing its reliability is very low. Therefore, computed tomography and X-ray images have been widely used. Artificial intelligence coupled with X-ray technologies has recently shown to be more effective in the diagnosis of this disease. With this motivation, a comparative analysis of fine-tuned deep learning architectures has been made to speed up the detection and classification of COVID-19 patients from other pneumonia groups. The models used for this analysis are MobileNetV2, ResNet50, InceptionV3, NASNetMobile, VGG16, Xception, InceptionResNetV2 DenseNet121, which have been fine-tuned using a new set of layers replaced with the head of the network. This research work has carried out an analysis on two datasets. Dataset-1 includes the images of three classes: Normal, COVID, and Pneumonia. Dataset-2, in contrast, contains the same classes with more focus on two prominent pneumonia categories: bacterial pneumonia and viral pneumonia. The research was conducted on 959 X-ray images (250 of Bacterial Pneumonia, 250 of Viral Pneumonia, 209 of COVID, and 250 of Normal cases). Using the confusion matrix, the required results of different models have been computed. For the first dataset, DenseNet121 has obtained a 97% accuracy, while for the second dataset, MobileNetV2 has performed best with an accuracy of 81%.

CONFLICT OF INTEREST

The authors declare that there is no conflict of interest related to this paper.

Open Research

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available in arxiv.org at https://arxiv.org/abs/2006.11988. These data were derived from the following resources available in the public domain: COVID-19 Image Data Collection, https://arxiv.org/abs/2006.11988

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Volume39, Issue3

Deep Neural Networks for Biomedical Data and Imaging / COVID‐19 Special Issue: Intelligent Solutions for Computer Communication‐Assisted Infectious Disease Diagnosis

March 2022

e12749

Automated COVID-19 detection in chest X-ray images using fine-tuned deep learning architectures

Abstract

CONFLICT OF INTEREST

Open Research

DATA AVAILABILITY STATEMENT

REFERENCES

Citing Literature

References

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Automated COVID-19 detection in chest X-ray images using fine-tuned deep learning architectures

Abstract

CONFLICT OF INTEREST

Open Research

DATA AVAILABILITY STATEMENT

REFERENCES

Citing Literature

References

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