Proceedings of the First International Conference on Artificial Intelligence, Smart Technologies and Communications (AISTC 2025)

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Enhancing Pneumonia X-ray Imaging Diagnosis through Advanced Super-Resolution Approach

Authors
Rania Saoudi1, *, Djamel Eddine Boudechiche1, Zoubeida Messali1
1ETA Laboratory, Department of Electronics, University Mohamed El Bachir El Ibrahimi of Bordj Bou Arreridj, El Anceur, Algeria
*Corresponding author. Email: rania.saoudi@univ-bba.dz
Corresponding Author
Rania Saoudi
Available Online 5 August 2025.
DOI
10.2991/978-94-6463-805-9_7How to use a DOI?
Keywords
Chest X-ray; deep learning; super-Resolution; attention mechanisms
Abstract

Obtaining higher-resolution Chest X-ray (CXR) images is often affected by significant challenges, resulting in low-resolution (LR) images that lead to misinterpretations which in turn impede accurate diagnosis. To address these limitations, we introduce in this paper an Iterative Transform-Spatial Feature Extraction Network (ITSFEN). This deep learning-based Super-Resolution (SR) approach utilizes a dual-feature extraction domain structure with dual attention mechanisms. It captures local features from the spatial domain and simultaneously extracts global features from the frequency domain within a hierarchical and iterative framework. By integrating attention mechanisms into this dual-domain feature extraction approach, it enhances its ability to perform feature extraction, significantly improving the resolution of LR CXR images. Our proposed method shows significant improvements over existing SR techniques, as proven by experimental results indicating superior image in terms of quantitative metrics and visual quality. This makes it a valuable tool for evaluating CXR imaging in clinical settings.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the First International Conference on Artificial Intelligence, Smart Technologies and Communications (AISTC 2025)
Series
Advances in Intelligent Systems Research
Publication Date
5 August 2025
ISBN
978-94-6463-805-9
ISSN
1951-6851
DOI
10.2991/978-94-6463-805-9_7How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Rania Saoudi
AU  - Djamel Eddine Boudechiche
AU  - Zoubeida Messali
PY  - 2025
DA  - 2025/08/05
TI  - Enhancing Pneumonia X-ray Imaging Diagnosis through Advanced Super-Resolution Approach
BT  - Proceedings of the First International Conference on Artificial Intelligence, Smart Technologies and Communications (AISTC 2025)
PB  - Atlantis Press
SP  - 49
EP  - 55
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-805-9_7
DO  - 10.2991/978-94-6463-805-9_7
ID  - Saoudi2025
ER  -