Channel and Spatial Attention in Chest X-Ray Radiographs: Advancing Person Identification and Verification with Self-Residual Attention Network

The proposed SRAN with self-residual attention provided a promising solution for biometric identification through chest X-ray imaging, showcasing its potential for accurate and reliable identity verification where traditional biometric approaches may fall short, especially in postmortem cases or forensic investigations. This methodology could play a transformative role in both biometric security and healthcare applications, offering a robust alternative modality for identity verification.

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Publication Details

Journal
Diagnostics
Published
2024-11-25
DOI
https://doi.org/10.3390/diagnostics14232655
Citations
5
Primary Topic
COVID-19 diagnosis using AI
Type
article
Field-Weighted Citation Impact
2.02
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article

Channel and Spatial Attention in Chest X-Ray Radiographs: Advancing Person Identification and Verification with Self-Residual Attention Network

Neesrin Ali Kurdi, Akram Bennour, Mohammed Al-Sarem, Samir Hammami et al.
5 citations
Diagnostics
COVID-19 diagnosis using AI
2.02
article

Channel and Spatial Attention in Chest X-Ray Radiographs: Advancing Person Identification and Verification with Self-Residual Attention Network

Neesrin Ali Kurdi, Akram Bennour, Mohammed Al-Sarem, Samir Hammami, Hazem Farah
article en
5 citations

Abstract

The proposed SRAN with self-residual attention provided a promising solution for biometric identification through chest X-ray imaging, showcasing its potential for accurate and reliable identity verification where traditional biometric approaches may fall short, especially in postmortem cases or forensic investigations. This methodology could play a transformative role in both biometric security and healthcare applications, offering a robust alternative modality for identity verification.

DiagnosticsVol. 14(23)
Taibah University (SA), Université Larbi Tébessi (DZ), Dhofar University (OM)
Reduced inequalities
Openalex Percentile: Top 12%
COVID-19 diagnosis using AI
2.02
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