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COVID-ConvNet:用于诊断COVID-19感染的卷积神经网络分类器

Ibtihal A L Alablani¹, Mohammed J F Alenazi¹

¹Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh P.O. Box 11451, Saudi Arabia.

Diagnostics (Basel, Switzerland)

|May 27, 2023

概括

一个新的深度学习模型,COVID-ConvNet,通过胸部X射线准确检测COVID-19. 这种人工智能工具实现了97.43%的准确性,有助于在疫情期间快速查患者.

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:

随着COVID-19的流行,全球健康面临着挑战.
胸部放射是识别COVID-19的一个关键查工具.
在COVID-19患者中观察到特有的放射性异常.

研究的目的:

介绍COVID-ConvNet,一个深层卷积神经网络 (DCNN).
评估COVID-ConvNet在从胸部X射线 (CXR) 扫描中检测COVID-19的有效性.

主要方法:

设计了一个深度学习模型,COVID-ConvNet.
该模型在来自COVID-19数据库的21,165张CXR图像上进行了训练和验证.
绩效是根据预测准确度进行评估的.

主要成果:

COVID-ConvNet实现了高预测准确率的97.43%.
该模型与现有的相关作品相比,表现出更高的性能,精度高达5.9%.

结论:

COVID-ConvNet是一种有效的深度学习工具,用于使用CXR检测COVID-19.
该模型显示了在流行病情景下改善患者查和诊断的巨大潜力.

关键词:

在美国,CNN是CNN.在 COVID-19 疫情中,胸部X射线胸部X射线冠状病毒冠状病毒病毒深度学习是一种深度学习.

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