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A Proposed Deep-Learning-Based Framework for Medical Image Communication, Storage and Diagnosis

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Abstract

Medical images rather than any other types of images need high storage space. The lack of storage facilities, especially in developing countries, encourages researchers to find solutions for this problem. Compression of medical images is a priority, although it leads to some loss in the stored images. This paper introduces a framework for medical image storage and retrieval for the purpose of diagnosis. This framework adopts decimation as a tool for image compression, while interpolation is used as a tool for further image reconstruction. The quality of the reconstructed images is evaluated with a scale-invariant feature transform (SIFT)-based technique. Another task involved in this paper is the automatic diagnosis from the reconstructed images based on deep learning. Different types of interpolation algorithms are investigated and compared in this framework for the process of medical image reconstruction.

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Author information

Authors and Affiliations

  1. Department of Electronics Technology, Faculty of Industrial Education, Beni-Suef University, Beni-Suef, Egypt

    Mostafa Ebied & Ayman Brisha

  2. Department of Electrical Engineering, Faculty of Engineering, Beni-Suef University, Beni-Suef, Egypt

    F. A. Elmisery

  3. The Higher Institute of Commercial Sciences, Al mahalla Al kubra, Algarbia, 31951, Egypt

    Noha A. El-Hag

  4. Smart Systems Engineering Laboratory, College of Engineering , Prince Sultan University , 11586, Riyadh, Saudi Arabia

    Ahmed Sedik

  5. Department of the Robotics and Intelligent Machines, Faculty of Artificial Intelligence, Kafr ElSheikh University, Kafr El Sheikh, Egypt

    Ahmed Sedik

  6. Security Engineering Lab, Computer Science Department, Prince Sultan University, 11586, Riyadh, Saudi Arabia

    Walid El-Shafai

  7. Department of Electronics and Electrical Communication Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Walid El-Shafai, Eman Soltan, Adel S. El-Fishawy, El-Sayed M. El-Rabaie & Fathi E. Abd El-Samie

  8. Department Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Ghada M. El-Banby

  9. Alblqaa Applied University, Salt, Jordan

    Nayel Al-Zubi

  10. Department of Electronics and Communications, Faculty of Engineering, Ain Shams University, Cairo, Egypt

    Abdelhalim Zekry

  11. Department of Electronics and Electrical Communications Engineering, Minia University, Minia, 61519, Egypt

    Ashraf A. M. Khalaf

  12. Department of Electrical and Electronic Engineering, University of Liverpool, Liverpool, UK

    Waleed Al-Nuaimy

  13. Department of Information Technology College of Computer and Information Sciences , Princess Nourah Bint Abdulrahman University , 11671, Riyadh, Saudi Arabia

    Fathi E. Abd El-Samie

Authors
  1. Mostafa Ebied
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  2. F. A. Elmisery
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  9. Ayman Brisha
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  12. Adel S. El-Fishawy
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  15. Fathi E. Abd El-Samie
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Corresponding author

Correspondence to Noha A. El-Hag.

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Ebied, M., Elmisery, F.A., El-Hag, N.A. et al. A Proposed Deep-Learning-Based Framework for Medical Image Communication, Storage and Diagnosis. Wireless Pers Commun 131, 2331–2369 (2023). https://doi.org/10.1007/s11277-022-09931-4

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