NAGNN:COVID-19的分类基于来自深图神经网络的邻居意识表示
Siyuan Lu1, Ziquan Zhu2, Juan Manuel Gorriz3
1School of Informatics University of Leicester Leicester UK.
概括
这项研究引入了一种新的人工智能系统,用于从CT扫描中检测COVID-19肺炎. 邻近意识图神经网络 (NAGNN) 在识别COVID-19病例方面表现出卓越的准确性和概括性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 2019年12月,COVID-19肺炎出现,导致全球健康和经济产生重大影响.
- 从医学成像中准确和快速诊断COVID-19对于有效的患者管理和疾病控制至关重要.
研究的目的:
- 开发基于人工智能的计算机辅助诊断系统,用于在胸部计算机断层扫描 (CT) 图像中自动检测COVID-19.
- 为了提高人工智能模型的准确性和概括能力,用于COVID-19分类.
主要方法:
- 使用深层卷积神经网络的转移学习来提取图像级表示 (ILR).
- 通过使用k-最近邻近算法构建ILR图,提出了一个新的邻近意识表示 (NAR).
- 开发了一个端到端的分类架构,邻近感知图形神经网络 (NAGNN),集成ILR和图形信息.
主要成果:
- 拟议的NAGNN模型与10种最先进的方法相比,显示出更高的性能.
- 从胸部CT图像中分类COVID-19的NAGNN表现出增强的概括能力.
- 使用私人和公共数据集进行实验验证.
结论:
- 开发的NAGNN系统有效检测COVID-19肺炎.
- 拟议的方法显示了将其整合到COVID-19临床诊断工作流中的潜力.
- 新的NAR方法提高了AI模型在医疗图像分析中的性能.
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