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Automated Detection of COVID-19 from Multimodal Imaging Data Using Optimized Convolutional Neural Network Model

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Abstract

The incidence of COVID-19, a virus that is responsible for infections in the upper respiratory tract and lungs, witnessed a daily rise in fatalities throughout the pandemic. The timely identification of COVID-19 can contribute to the formulation of strategies to control the disease and the selection of an appropriate treatment pathway. Given the necessity for broader COVID-19 diagnosis, researchers have developed more advanced, rapid, and efficient detection methods. By conducting an initial comparative analysis of various widely used convolutional neural network (CNN) models, we determine an appropriate CNN model. Subsequently, we enhance the chosen CNN model using the feature fusion strategy from multi-modal imaging datasets. Moreover, the Jaya optimization technique is employed to determine the optimal weighting for merging these dual features into a single feature vector. An SVM classifier is employed to categorize samples as either COVID-19 positive or negative. For the purpose of experimentation, a standard dataset consisting of 10,000 samples is used, divided equally between COVID-19 positive and negative classes. The experimental outcomes demonstrate that the proposed fine-tuned system, coupled with optimization techniques for multi-modal data, exhibits superior performance, achieving accuracy rates of 98.7% as compared to the existing state-of-the-art network models.

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Acknowledgements

The authors would like to thank the editor and reviewers for their valuable comments and suggestions, which helped us to greatly improve the quality of the manuscript. The authors also extend their appreciation to the Deanship of Scientific Research at King Khalid University, for funding this work through a large group Research Project under grant number RGP2/238/44.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, 601103, India

    S. Veluchamy & R. Annamalai

  2. School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, India

    S. Sudharson

  3. Department of Information Technology, University of Tabuk, Tabuk, 71491, Saudi Arabia

    Zaid Bassfar

  4. College of Computing and Information Technology, University of Tabuk, Tabuk, 71491, Saudi Arabia

    Amer Aljaedi

  5. Department of Mathematics, College of Science, King Khalid University, Abha, 61413, Saudi Arabia

    Sajjad Shaukat Jamal

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  1. S. Veluchamy
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Correspondence to S. Sudharson.

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Veluchamy, S., Sudharson, S., Annamalai, R. et al. Automated Detection of COVID-19 from Multimodal Imaging Data Using Optimized Convolutional Neural Network Model. J Digit Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-024-01077-y

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