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Classifying chest x-rays for COVID-19 through transfer learning: a systematic review

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Abstract

This study makes a comprehensive assessment of the predominant Transfer Learning (TL) techniques employed for the classification of COVID-19 cases in Chest X-rays (CXR) images. The methodologies have been selected on the basis of their merits and demerits, suitability, and possible impact on the development of the region being studied. The study examines the various methods of TL employed in the classification of COVID-19 cases with the objective to gain a deeper understanding about all the aspects of these methodologies. It can be of great significance for the researchers and medical professionals in making well-informed decisions about the implementation of these techniques to improve the precision and effectiveness of COVID-19 diagnosis. The practical consequences of these techniques help in early identification of such cases for having a suitable intervention. As many as 48 studies conducted during the period 2020–2023 have been included in the current research work for having an assessment about the problem under investigation. The study has specifically focused on transfer learning-based models utilized for the identification of COVID-19 through CXR pictures. It highlights the challenges posed by dataset dynamics, methodological variations, and performance metrics of different models.

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The authors declare that the data supporting the findings of this study are available within the paper.

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Authors and Affiliations

  1. Thapar Institute of Engineering & Technology, Patiala, Punjab, India

    Devanshi Mallick

  2. Wipro Limited, Gurugram, India

    Arshdeep Singh

  3. School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore

    Eddie Yin-Kwee Ng

  4. Computer Science Engineering Department, Thapar Institute of Engineering & Technology, Patiala, Punjab, India

    Vinay Arora

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  3. Eddie Yin-Kwee Ng
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  4. Vinay Arora
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Contributions

Conceptualization, Vinay Arora and Eddie Yin-Kwee Ng; Data synthesis, Devanshi Mallick, Arshdeep Singh; Formal analysis, Eddie Yin-Kwee Ng; Investigation, Vinay Arora; Methodology, Vinay Arora and Devanshi Mallick; Project administration, Eddie Yin-Kwee Ng and Vinay Arora; Visualization, Devanshi Mallick, Arshdeep Singh, Vinay Arora; Writing – original draft, Devanshi Mallick, Vinay Arora, Eddie Yin-Kwee Ng.

Corresponding author

Correspondence to Devanshi Mallick.

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Appendix 1: A quality assessment forms

Appendix 1: A quality assessment forms

A.1. Screening question

 

Section – 1

Does the research paper focus on classifying chest X-rays for COVID-19 through transfer learning?

Yes

No

Consider:

 

- The work explores the categorization of chest X-rays for COVID-19 utilizing transfer learning, using several types of studies such as case studies, experimental studies, or research publications. It is crucial to highlight that the classification process entails distinguishing COVID-19 situations. Section 1 is evaluated first, and if the outcome is favorable, Sect. 2 is then considered

Section – 1 is evaluated first. If the reply is positive, only then Section – 2 will be considered

 

A.2. Screening question

 

Section – 2

Does the study article employ a particular methodology to categorize chest X-rays for COVID-19 using transfer learning?

Yes

No

Consider:

 

- Is the main emphasis of the study the categorization of chest X-rays for COVID-19 using transfer learning?

- Does the study adhere to any specific strategy for this classification?

If the study's main emphasis is on classifying chest X-rays for COVID-19 or its methodology, proceed to Section – 3

 

A.3. Detailed questions

 

Section -3

Did the study examine the methodology used to categorize chest X-rays for COVID-19, or did it focus on a specific technique within that approach?

Yes

No

Consider:

 

- How are the procedures for categorizing chest X-rays for COVID-19 classified?

- Does the study specifically state the methodology used to categorize chest X-rays for COVID-19, or is it deduced from the study?

- Are graphical approaches used for any purpose?

- Does the given data provide sufficient information to do a comparative analysis?

 

Was the data provided sufficient for conducting a comparison analysis?

Yes

No

Consider:

 

Have the fundamental criteria for comparison been fully examined?

 

What is the subject system utilized for categorizing chest X-rays for COVID-19 using transfer learning?

Yes

No

Consider:

 

Did the subject system have a major impact under the categorization approach?

 

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Cite this article

Mallick, D., Singh, A., Ng, E.YK. et al. Classifying chest x-rays for COVID-19 through transfer learning: a systematic review. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18924-3

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  • DOI: https://doi.org/10.1007/s11042-024-18924-3

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