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使用胸部X射线细分和深度学习来检测COVID-19的严重程度

Tinku Singh¹, Suryanshi Mishra², Riya Kalra³

¹School of Information and Communication Engineering, Chungbuk National University, Cheongju, South Korea.

Scientific reports

|August 27, 2024

概括

这项研究提出了使用胸部放射 (CXR) 进行准确的COVID-19检测和严重程度评估的深度学习框架. 人工智能模型有助于早期诊断和患者管理,改善临床决策.

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对全球健康和日常生活产生了重大影响,需要有效的诊断工具.
胸部放射 (CXR) 提供了CT扫描用于COVID-19诊断的实用替代方案,尽管存在潜在的灵敏度限制.
准确的分类和严重程度预测对于有效的患者管理和资源分配至关重要.

研究的目的:

开发和验证使用CXR图像进行COVID-19分类和严重程度预测的深度学习框架.
为了提高CXR用于COVID-19检测的诊断准确度.
改善COVID-19严重程度的评估,以获得更好的临床结果.

主要方法:

一个深度学习框架,集成了U-Net用于肺部细分,一个Convolution-capsule网络用于分类,以及ResNet50,VGG-16和DenseNet201用于严重性评估.
通过U-Net,肺部细分精度达到0.9924.
该分类模型显示了高的真实阳性率:COVID-19为86%,肺炎为93%,正常病例为85%.

主要成果:

与ResNet50和VGG-16.6相比,DenseNet201在COVID-19严重程度评估中表现出更高的准确性.
该框架实现了高分类性能,使用95%置信区间验证了结果.
综合深度学习方法在分析CXR图像方面被证明是可靠和强大的.

结论:

开发的深度学习框架有效地对COVID-19进行分类,并从CXR图像中预测疾病严重程度.
这种人工智能驱动的方法可以提高COVID-19的早期检测和评估,从而支持改善患者护理.
该研究强调了将先进的人工智能技术与放射性成像相结合的潜力,以支持临床决策.

关键词:

布里克西亚的得分是什么?在 COVID-19 疫情中,囊网络是一个囊网络.胸部X射线胸部X射线深度学习是一种深度学习.

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