一个基于学习的联合隐私保护图像处理框架,用于从CT扫描中检测大脑瘤.
Abdullah Al-Saleh1, Ghanshyam G Tejani2,3, Shailendra Mishra4
1Department of Computer Engineering, College of Computer and Information Sciences, Majmaah University, Majmaah, 11952, Saudi Arabia.
Scientific reports
|July 2, 2025
概括
这项研究引入了一个新的Aniso-ResCapHGBO-Net框架,用于使用分散的AI来保护隐私的脑瘤检测. 该模型实现了高精度,提高了早期诊断,同时保护了患者数据.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 数据 隐私 数据 隐私 数据
背景情况:
- 准确的脑瘤检测对于患者的治疗结果至关重要.
- 由于集中式数据存储,传统的深度学习模型带来了隐私和监管挑战.
- 需要分散的系统来解决医疗保健中的数据异质性和隐私问题.
研究的目的:
- 为大脑瘤检测开发一个保护隐私的,分散的框架.
- 为了增强特征提取和保存医疗图像中的空间信息.
- 确保私有区块链上的安全和无改模型更新.
主要方法:
- 不同类型的残留囊混合体大猩猩鱼优化网络框架 (Aniso-ResCapHGBO-Net).
- 集成ResNet-50和囊网络,用于先进的特征提取.
- 混合大猩猩的优化算法 (HGBOA) 用于特征选择.
- 预处理:异型扩散过,形态操作,基于相互信息的图像注册.
- 通过以太坊私有区块链和SHA-256哈希进行安全更新.
主要成果:
- 在基准CT脑瘤数据集上实现了99.07%的准确性,98.54%的精度和99.82%的灵敏度.
- 证明了虚假阴性和虚假阳性的减少.
- 以分散的,保护隐私的方式验证了框架的有效性.
结论:
- 安尼索-ResCapHGBO-Net框架为准确的脑瘤检测提供了一个强大的解决方案.
- 这种分散和保护隐私的方法解决了关键的医疗保健数据挑战.
- 这种方法成功地平衡了高诊断性能与严格的数据保护要求.
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