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一个新兴的COVID-19CT扫描分类网络,使用集体深度转移学习模型进行分类

Kolsoum Yousefpanah¹, M J Ebadi², Sina Sabzekar³

¹Department of Statistics, University of Guilan, Rasht, Iran.

|June 15, 2024

概括

人工智能 (AI) 模型,包括CT6-CNN和集体深度学习,在CT扫描中诊断COVID-19时显示出高准确度. 这些人工智能方法对于早期检测和有效管理病毒至关重要.

科学领域:

医疗成像医学成像
人工智能的人工智能
计算生物学计算生物学

背景情况:

在全球范围内,COVID-19的流行病已经造成了数百万人的死亡,需要快速准确的诊断工具.
早期发现COVID-19对于控制病毒传播和迅速启动患者治疗至关重要.
计算机断层扫描 (CT) 图像是诊断COVID-19的宝贵资源.

研究的目的:

开发和评估基于人工智能 (AI) 的模型,用于使用CT扫描诊断COVID-19.
为了比较新型CNN模型 (CT6-CNN) 与整体深度转移学习模型的性能.

主要方法:

设计了一个名为CT6-CNN的卷积神经网络 (CNN) 模型.
开发了两个集体深度转移学习模型,集成了Xception,ResNet-101,DenseNet-169和CT6-CNN.
这些模型在SARS-CoV-2CT数据集上进行了训练和验证,该数据集包括2481次CT扫描.

主要成果:

CT6-CNN模型的准确度为94.66%,精度为94.67%,灵敏度为94.67%,F1得分为94.65%.
集体深度学习模型表现出卓越的性能,达到99.2%的准确性.
实验结果证实了开发的人工智能模型的高效性,特别是合奏方法.

结论:

人工智能驱动的诊断工具,特别是集体深度学习模型,显示出从CT图像中准确有效地检测COVID-19的重大前景.
开发的模型可以帮助临床医生及时诊断,有助于更好地控制流行病和患者的治疗结果.

关键词:

人工智能的人工智能是人工智能.在 COVID-19 疫情中,深度学习是一种深度学习.机器学习机器学习这是一个软选票.

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