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一种深度转移学习方法来检测COVID-19并探索糖尿病患者的归属感

Ijaz Ahmad¹, Arcangelo Merla², Farman Ali³

¹Digital Transition, Innovation and Health Service, Leonardo da Vinci Telematic University, Chieti, Italy.

Frontiers in public health

|November 29, 2023

概括

这项研究介绍了CIDICXR-Net50,一种基于ResNet-50的新型模型,用于从胸部X射线检测COVID-19. 该模型实现了高精度,为RT-PCR提供了更快的替代方案,以快速诊断COVID-19.

科学领域:

医疗成像医学成像
人工智能的人工智能
传染性疾病传染性疾病

背景情况:

COVID-19带来了重大风险,特别是在老年人和糖尿病患者身上.
及时检测COVID-19对于患者管理和疫情控制至关重要.
目前的黄金标准RT-PCR是耗时且资源密集的.

研究的目的:

利用胸部X射线图像开发一种高效准确的COVID-19检测方法.
为了评估新型深度学习模型CIDICXR-Net50的性能,用于COVID-19分类.
探索糖尿病和COVID-19严重程度之间的潜在联系.

主要方法:

通过修改分类层,开发了一个基于ResNet-50的转移学习 (TL) 模型CIDICXR-Net50.
该模型在3,923张胸部X射线图像 (COVID-19,病毒性肺炎,正常) 的数据集上进行了训练.
对其他六个预训练模型的性能进行了评估.

主要成果:

在CIDICXR-Net50模型实现了99.11%的准确性,99.15%的精度和99.15%的回忆.
该模型在与其他评估模型相比,表现出了卓越的性能.
该研究还调查了COVID-19和糖尿病之间的相关性.

结论:

CIDICXR-Net50提供了一个高度准确和高效的替代方案,用于通过胸部X射线检测COVID-19.
这种人工智能驱动的方法可以帮助在疫情爆发期间快速诊断.
需要进一步的研究来了解糖尿病和COVID-19结果之间的关系.

关键词:

在 COVID-19 疫情中,胸部X射线胸部X射线卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.在糖尿病中,糖尿病是血糖性糖尿病.长时间的Covid.转移学习转移学习

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