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

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 image...

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Bibliographic Details
Published in:Wireless personal communications 2023-08, Vol.131 (4), p.2331-2369
Main Authors: Ebied, Mostafa, Elmisery, F. A., El-Hag, Noha A., Sedik, Ahmed, El-Shafai, Walid, El-Banby, Ghada M., Soltan, Eman, Al-Zubi, Nayel, Brisha, Ayman, Zekry, Abdelhalim, Khalaf, Ashraf A. M., El-Fishawy, Adel S., El-Rabaie, El-Sayed M., Al-Nuaimy, Waleed, El-Samie, Fathi E. Abd
Format: Article
Language:English
Subjects:
Algorithms
Communications Engineering
Computer Communication Networks
Deep learning
Developing countries
Diagnosis
Engineering
Image compression
Image quality
Image reconstruction
Image storage
Interpolation
LDCs
Medical imaging
Networks
Signal,Image and Speech Processing
Storage facilities
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Summary: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.
ISSN:0929-6212
1572-834X
DOI:10.1007/s11277-022-09931-4