EDG-Net:基于加密和解密的Gan-attention网络用于医疗物联网和远程医疗中的CT图像
IEEE journal of biomedical and health informatics
|December 23, 2025
概括
我们开发了EDG-Net,这是一个用于IoMT和远程医疗的安全CT图像加密和解密的新型网络. 这种方法增强了数据安全性,并确保了高准确度的图像重建,保护敏感的患者信息.
科学领域:
- 医疗成像医学成像
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 医疗物联网 (IoMT) 和远程医疗使医疗数据的共享成为可能,包括敏感的CT图像.
- 由于易受第三方攻击,在传输过程中保护CT图像中的私人患者信息至关重要.
- 现有的加密方法可能会损害图像保真性或安全性.
研究的目的:
- 为CT图像提出一个基于加密和解密的高级Gan-attention网络 (EDG-Net).
- 在IoMT和远程医疗环境中增强CT图像加密的安全性和稳定性.
- 为了提高解密的CT图像的准确性和真实性.
主要方法:
- 开发EDG-Net,包括一个生成器,两个区分器,注意力域转移和自适应性规范化.
- 实施CT图像的双重加密和解密策略.
- 利用训练的网络参数作为加密和解密的秘密密钥.
主要成果:
- 在加密安全性和稳定性方面,EDG-Net表现出卓越的性能.
- 拟议的方法在解密的纯文本图像中实现了高保真性.
- 定性和定量分析证实了公共和私人数据集的有效性.
结论:
- 在IoMT和远程医疗中,EDG-Net为CT图像加密和解密提供了安全有效的解决方案.
- 双重加密策略显著提高了数据保护和图像重建的准确性.
- 网络参数作为强大的秘密密钥,提高整体系统安全性.
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