COVID-19CT+:用于COVID-19追溯分析的CT图像的公共数据集
Yihao Sun1, Tianming Du1, Bin Wang2
1Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China.
这项研究介绍了COVID-19CT+,这是针对COVID-19和肺炎分类的胸部CT扫描的大数据集. 机器和深度学习模型表现出不同的性能,突出了对先进诊断算法的需求.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 肺部病理学 肺部病理学
背景情况:
- COVID-19 代表着一个重大的全球卫生危机.
- 准确有效地诊断COVID-19至关重要.
- 胸部CT成像是诊断肺炎的关键工具,包括COVID-19.
研究的目的:
- 介绍COVID-19CT+数据集,这是一个全面的胸部CT图像集合.
- 用CT图像评估用于COVID-19诊断的经典机器学习和深度学习分类器的性能.
- 分析各种算法和时间段的分类性能差异.
主要方法:
- 开发和发布COVID-19CT+数据集,包括来自1333名患者的409,619张CT图像 (COVID-19和社区获得的肺炎).
- 为图像分类任务实施和测试13个经典机器学习和5个深度学习分类器.
- 两个实验设置:COVID-19与白色肺病,以及社区获得的肺炎与COVID-19.
主要成果:
- 与传统机器学习 (97.3%) 相比,深度学习模型在分类COVID-19与白肺病方面取得了更高的最大精度 (97.9%).
- 在将社区获得的肺炎与COVID-19分类时,传统的机器学习达到94.6%的精度,而深度学习达到91.9%.
- 性能在不同的分类器和实验条件之间有很大的差异,这表明诊断方法的不断变化的性质.
结论:
- COVID-19CT+数据集是COVID-19和社区获得性肺炎研究的最大数据集之一.
- 该数据集预计将促进对新型自动诊断算法的研究,以提高准确性和效率.
- 进一步开发人工智能驱动的诊断工具对于管理COVID-19等呼吸道疾病至关重要.
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