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Severity of lung infection identification and classification using optimization-enabled deep learning with IoT

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Abstract

A major disease affecting individuals irrespective of the different ages is lung disease and this problem is a result of different causes. The recent spread of COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected a huge community worldwide and has impacted the respiratory system adversely. The infection severity can be determined by inspecting the using X-ray images of the lung. In this work, a multilevel classification approach is presented, wherein the lung severity and COVID-19 prediction are executed based on Deep Learning (DL) technique. The contribution of this research is three-fold: (1) a novel Political Golden Search Algorithm (PGSA) was devised for routing the data accumulated from the nodes over the Internet of Things (IoT), (2) first-level classification was performed using the developed SqueezeNet-based technique, and it is optimized by the devised Taylor Political Golden Search Optimization (TPGSO) algorithm, to detect if COVID-19 is present or not, (3) second-level classification is accomplished using the TPGSO-deep convolutional neural network (DCNN) to categorize lung infection severity. The presented TPGSO-DCNN for second-level classification is examined for its performance based on testing accuracy, test negative rate (TNR), and test positive rate (TPR), and is established to have obtained values of 0.922, 0.926, and 0.909, respectively.

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Data availability statement

The data underlying this article are available in Deep COVID Dataset is taken from https://github.com/shervinmin/DeepCovid.

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Acknowledgements

I would like to express my very great appreciation to the co-authors of this manuscript for their valuable and constructive suggestions during the planning and development of this research work.

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This research did not receive any specific funding.

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Authors and Affiliations

  1. Department of Mathematics & Computer Science, Modern College of Business and Sciences, Bowshar, Sultanate of Oman

    P. Vijaya

  2. Department Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, India

    Satish Chander

  3. Department of Cyber Security, NMAM Institute of Technology, NITTE (Deemed to be University), Nitte, India

    Roshan Fernandes

  4. Department of Computer Science and Engineering, NMAM Institute of Technology, NITTE (Deemed to be University), Nitte, India

    Anisha P. Rodrigues

  5. Department of School of Computer Science and Information Technology, Symbiosis Skills and Professional Univeristy, Pune, India

    R. Maheswari

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All authors have made substantial contributions to conception and design, revising the manuscript, and the final approval of the version to be published. Also, all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Correspondence to P. Vijaya.

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Communicated by X. Yang.

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Vijaya, P., Chander, S., Fernandes, R. et al. Severity of lung infection identification and classification using optimization-enabled deep learning with IoT. Multimedia Systems 30, 107 (2024). https://doi.org/10.1007/s00530-024-01275-2

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