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医疗保健领域的人工智能:使用胸部X射线图像对深度学习网络进行比较

M Akif Yenikaya¹, Gökhan Kerse¹, Onur Oktaysoy¹

¹Faculty of Economics and Administrative Sciences, Department of Management Information Systems, Kafkas University, Kars, Türkiye.

Frontiers in public health

|April 25, 2024

概括

深度学习模型通过胸部X射线准确诊断了COVID-19和肺炎. ResNet101模型实现了最高的诊断成功率,为96.32%.

科学领域:

医疗成像医学成像
医疗保健中的人工智能
深度学习 (Deep Learning) 是一种深度学习.

背景情况:

人工智能 (AI) 正在彻底改变医疗保健.
深度学习,AI的一个子集,在医疗应用中显示出显著的潜力.

研究的目的:

评估深度学习网络在从胸部X射线诊断疾病中的有效性.
为了比较ResNet101,AlexNet,GoogLeNet和Xception在疾病分类方面的性能.

主要方法:

使用了1680张胸部X射线的数据集,包括COVID-19,病毒性肺炎和正常病例.
通过旋转应用了数据增强.
实施了70%/30%的培训和验证分配.
分析了四个深度学习网络 (ResNet101,AlexNet,GoogLeNet,Xception) 的情况.

主要成果:

深度学习网络在分类COVID-19,病毒性肺炎和正常胸部X射线方面取得了很高的成功.
在ResNet101中,最高准确率为96.32%,为最高准确率.
这些模型在区分疾病状态方面证明是有效的.

结论:

深度学习是医疗保健中疾病诊断的可行工具.
人工智能驱动的诊断可以支持医疗专业人员并改善医疗保健管理.
这项技术可以提高诊断的准确性和效率.

关键词:

在 COVID-19 疫情中,人工智能的人工智能是人工智能.深度学习是一种深度学习.医疗保健组织医疗保健组织医疗保健部门医疗保健部门病毒性肺炎的肺炎是一种病毒性肺炎.

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