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Deep Learning Based Entropy Controlled Optimization for the Detection of Covid-19

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Abstract

Emerging technological advancements open the door for employing deep learning-based methods in practically all spheres of human endeavor. Because of their accuracy, deep learning algorithms can be used in healthcare to categorize and identify different illnesses. The recent coronavirus (COVID-19) outbreak has significantly strained the global medical system. By using medical imaging and PCR testing, COVID-19 can be diagnosed. Since COVID-19 is highly transmissible, it is generally considered secure to analyze it with a chest X-ray. To distinguish COVID-19 infections from additional infections that are not COVID-19 infections, a deep learning-based entropy-controlled whale optimization (EWOA) with Transfer Learning is suggested in this paper. The created system comprises three stages: a preliminary processing phase to remove noise effects and resize the image, then a deep learning architecture using a pre-trained model to extract features from the pre-processed image. After extracting the features, optimization is carried out. EWOA is utilized to combine and optimize the optimum features. A softmax layer is used to reach the final categorization. Various activation functions, thresholds, and optimizers are used to assess the systems. Numerous metrics for performance are utilized to measure the performance of the offered methodologies for assessment. Through an accuracy of 97.95%, the suggested technique accurately categorizes four classes, including COVID-19, viral pneumonia, chest infection, and routine. Compared to current methodologies found in the literature, the proposed technique exhibits advantages regarding accuracy.

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Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

Aisha M. Alqahtani from Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R52), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

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Authors and Affiliations

  1. Department of Artificial Intelligence, Shanxi Polytechnic College, Taiyuan, 030006, China

    Jiong Chen

  2. College of Computer Science and Engineering, University of Hafr Albatin, 31991, Hafar Albatin, Saudi Arabia

    Abdullah Alshammari

  3. Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, 16273, Al-Kharj, Saudi Arabia

    Mohammed Alonazi

  4. Department of mathematical sciences, College of Science, Princess Nourah bint Abdulrahman University, P. O. Box 84428, 11671, Riyadh, Saudi Arabia

    Aisha M. Alqahtani

  5. Department of Computer Science, College of Computer and Information Sciences, Majmaah University, 11952, Al-Majmaah, Saudi Arabia

    Sara A. Althubiti

  6. Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Medan, 20155, Indonesia

    Romi Fadillah Rahmat

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  1. Jiong Chen
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  2. Abdullah Alshammari
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Contributions

Jiong Chen: Conceptualization, Methodology, Formal analysis, Supervision, Writing—original draft, Writing—review & editing.

Abdullah Alshammari: Investigation, Data Curation, Validation, Resources, Writing—review & editing.

Mohammed Alonazi: Writing—original draft, Writing—review & editing.

Aisha M. Alqahtani: Data Curation, Validation, Resources, Writing—review & editing.

Sara A Althubiti: Validation, Resources, Writing—review & editing.

Romi Fadillah Rahmat: Writing—original draft, Writing—review & editing.

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Correspondence to Jiong Chen.

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Chen, J., Alshammari, A., Alonazi, M. et al. Deep Learning Based Entropy Controlled Optimization for the Detection of Covid-19. J Grid Computing 22, 53 (2024). https://doi.org/10.1007/s10723-024-09766-2

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