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利用卷积神经网络和散列技术来安全地分类麻疹疾病

Essam Abdellatef¹, Alshimaa H Ismail², M I Fath Allah³

¹Department of Electrical Engineering, Faculty of Engineering, Sinai University, El-Arish, 45511, Egypt.

Scientific reports

|November 4, 2024

概括

一个新的深度学习模型,CanDark,使用皮肤图像准确诊断水. 这种人工智能方法提高了患者的隐私,同时实现了高诊断性能,为疾病检测提供了有前途的工具.

科学领域:

医疗信息学医疗信息学
人工智能的人工智能
皮肤病学皮肤病学

背景情况:

由于国际传播, (MPX) 在2022年宣布全球卫生紧急情况.
早期的MPX诊断是具有挑战性的,因为症状与水和麻疹等其他疾病重叠.
人工智能 (AI),机器学习 (ML) 和深度学习 (DL) 对快速,准确的疾病检测充满希望.

研究的目的:

提出和评估CanDark模型,一个端到端的深度学习系统用于天花诊断.
在诊断模型中整合可取消生物识别,以保护患者数据隐私.
将CanDark的性能与现有的最先进的MPX诊断方法进行比较.

主要方法:

开发了CanDark,这是一个基于DarkNet-53 CNN的深度学习模型,用于从MPX皮肤病变图像中提取特征.
实施和评估了各种可取消的生物识别技术:生物哈希,多层感知子 (MLP) 哈希,基于高斯随机投影的最大指数哈希 (IoM-GRP) 和基于均随机排列的最大指数哈希 (IoM-URP).
使用准确性,特异性,精度,回忆和f-score指标评估模型性能.

主要成果:

与其他MPX诊断方法相比,CanDark模型,特别是使用IoM-URP可取消技术,表现出优越的性能.
实现了高诊断指标:98.81%的准确性,98.73%的特异性,98.9%的精度,97.02%的回忆力,97.95%的f-score.
可取消生物识别的整合有效地保护了患者信息,同时保持了高诊断准确度.

结论:

该CanDark模型提供了一个高度准确和保护隐私的AI驱动的解决方案来诊断麻疹.
这种深度学习框架在打击MPX方面取得了重大进展,特别是在资源有限的环境中.
该研究强调了将可取消生物识别集成到人工智能诊断工具中的潜力,以提高数据安全性.

关键词:

而暗网-53则是如此.在美国,CNN是CNN.可取消的技术可以取消.的水是一种天花.

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