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在智能医疗物联网系统中,用于基于MRI的脑瘤检测的安全混合深度学习

Nermeen Gamal Rezk¹, Samah Alshathri², Amged Sayed^3,4

¹Department of Computer and Systems Engineering, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh 33516, Egypt.

Diagnostics (Basel, Switzerland)

|March 13, 2025

概括

这项研究引入了一种使用加密的MRI图像和深度学习来分类脑瘤的安全方法. 该方法确保了患者数据的隐私,同时保持了瘤检测的高准确性,这对于医疗保健的早期诊断至关重要.

科学领域:

医疗成像医学成像
人工智能的人工智能
网络安全网络安全

背景情况:

大脑瘤具有攻击性,导致高死亡率.
目前的诊断依赖于侵入性活检,往往会推迟到手术.
需要自动分类来加快诊断和减少错误.

研究的目的:

用MRI图像开发一个安全和自动化的脑瘤分类系统.
将加密技术与深度学习模型相结合,以提高数据安全性.
为了确保诊断的准确性,即使使用加密的医疗数据,也会保持.

主要方法:

提出了一种混合深度学习模型,将VGG16和深度神经网络 (DNN) 结合起来.
集成的混沌和阿诺德加密技术来保护MRI图像.
对加密的MRI数据集的分类性能进行了评估.

主要成果:

通过混乱加密 (93.75%) 和阿诺德加密 (94.1%) 实现了高分类准确度.
证明了加密图像可以有效地分类,而不会影响诊断性能.
报告的精度,回忆和F-score值在两个加密方法中都超过93%.

结论:

混合深度学习方法为MIoT系统中大脑瘤检测提供了安全有效的解决方案.
在分类之前对MRI图像进行加密,可以确保患者数据的保密性.
这种方法支持早期和安全的诊断,赋予全球卫生保健专业人员权力.

关键词:

医疗物联网医疗物联网脑瘤检测脑瘤检测脑瘤检测混沌和阿诺德的算法.深度学习是一种深度学习.已加密的MRI图像被使用.混合型模型混合型模型混合型模型安全的诊断安全的诊断.

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