Farah Hazem, Bennour Akram, Kurdi Neesrin Ali, Hammami Samir, Al-Sarem Mohammed
Laboratory of Mathematics, Informatics and Systems (LAMIS), Echahid Chiekh Larbi Tebessi University, Tebessa 12002, Algeria.
College of Computer Science and Engineering, Taibah University, Medina 41477, Saudi Arabia.
Diagnostics (Basel). 2024 Nov 25;14(23):2655. doi: 10.3390/diagnostics14232655.
BACKGROUND/OBJECTIVES: In contrast to traditional biometric modalities, such as facial recognition, fingerprints, and iris scans or even DNA, the research orientation towards chest X-ray recognition has been spurred by its remarkable recognition rates. Capturing the intricate anatomical nuances of an individual's skeletal structure, the ribcage of the chest, lungs, and heart, chest X-rays have emerged as a focal point for identification and verification, especially in the forensic field, even in scenarios where the human body damaged or disfigured. Discriminative feature embedding is essential for large-scale image verification, especially in applying chest X-ray radiographs for identity identification and verification. This study introduced a self-residual attention-based convolutional neural network (SRAN) aimed at effective feature embedding, capturing long-range dependencies and emphasizing critical spatial features in chest X-rays. This method offers a novel approach to person identification and verification through chest X-ray categorization, relevant for biometric applications and patient care, particularly when traditional biometric modalities are ineffective.
The SRAN architecture integrated a self-channel and self-spatial attention module to minimize channel redundancy and enhance significant spatial elements. The attention modules worked by dynamically aggregating feature maps across channel and spatial dimensions to enhance feature differentiation. For the network backbone, a self-residual attention block (SRAB) was implemented within a ResNet50 framework, forming a Siamese network trained with triplet loss to improve feature embedding for identity identification and verification.
By leveraging the NIH ChestX-ray14 and CheXpert datasets, our method demonstrated notable improvements in accuracy for identity verification and identification based on chest X-ray images. This approach effectively captured the detailed anatomical characteristics of individuals, including skeletal structure, ribcage, lungs, and heart, highlighting chest X-rays as a viable biometric tool even in cases of body damage or disfigurement.
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.
背景/目的：与传统生物识别方式（如面部识别、指纹识别、虹膜扫描甚至DNA识别）不同，胸部X光识别因其显著的识别率而受到研究关注。胸部X光能够捕捉个体骨骼结构、胸腔、肺部和心脏等复杂的解剖细节，已成为身份识别和验证的焦点，特别是在法医领域，即使在人体受损或毁容的情况下也适用。判别特征嵌入对于大规模图像验证至关重要，尤其是在将胸部X光片用于身份识别和验证时。本研究引入了一种基于自残差注意力的卷积神经网络（SRAN），旨在实现有效的特征嵌入，捕捉远距离依赖关系并强调胸部X光中的关键空间特征。该方法通过胸部X光分类提供了一种新的人员识别和验证方法，适用于生物识别应用和患者护理，特别是在传统生物识别方式无效的情况下。
SRAN架构集成了自通道和自空间注意力模块，以最小化通道冗余并增强重要的空间元素。注意力模块通过跨通道和空间维度动态聚合特征图来增强特征区分。对于网络主干，在ResNet50框架内实现了一个自残差注意力块（SRAB），形成了一个使用三元组损失训练的连体网络，以改善用于身份识别和验证的特征嵌入。
通过利用NIH ChestX-ray14和CheXpert数据集，我们的方法在基于胸部X光图像的身份验证和识别准确性方面显示出显著提高。这种方法有效地捕捉了个体的详细解剖特征，包括骨骼结构、胸腔、肺部和心脏，突出了胸部X光即使在身体受损或毁容的情况下也是一种可行的生物识别工具。
所提出的具有自残差注意力的SRAN为通过胸部X光成像进行生物识别提供了一个有前景的解决方案，展示了其在传统生物识别方法可能不足的情况下进行准确可靠身份验证的潜力，特别是在死后案例或法医调查中。这种方法在生物识别安全和医疗保健应用中都可能发挥变革性作用，为身份验证提供一种强大的替代方式。
