基于COVID-19诊断的放射图像的复杂网络分类,用于COVID-19诊断
Weiguang Liu1, Rafael Delalibera Rodrigues2, Jianglong Yan2
1School of Computer Science, Zhongyuan University of Technology, ZhengZhou, China.
PloS one
|September 1, 2023
概括
这项研究引入了一种新的基于网络的方法,用于COVID-19胸部X射线分类的碎形维度和四树. 该技术有效地区分健康和COVID-19病例,为医疗图像分析提供了竞争性的方法.
科学领域:
- 医疗成像医学成像
- 网络科学 网络科学
- 机器学习 机器学习
背景情况:
- 胸部X射线显示了健康和COVID-19病例之间的几何复杂性差异.
- 传统的特征提取方法与复杂的现实世界数据集作斗争.
- 复杂网络提供先进的模式特征和数据关系分析.
研究的目的:
- 开发一种基于网络的分类技术,用于使用胸部X射线进行COVID-19诊断.
- 为了利用碎形维度和四边形树来提取特征.
- 结合复杂性测量和复杂网络,以提高分类.
主要方法:
- 应用了碎形维度和四边形树来从胸部X射线图像中提取特征.
- 利用复杂的网络分析来建模空间,拓和功能关系.
- 开发了一种修改的高层分类技术,整合了这些方法.
- 评估了网络措施,选择了类区别的可传输性.
主要成果:
- 拟议的方法在Kaggle COVID-19放射数据库上实现了高分类准确性.
- 基于网络的方法证明了与现有的最先进技术的竞争力.
- 可传输性测量在区分网络类别方面被证明是有效的.
结论:
- 复杂度测量和复杂网络的结合方法为COVID-19胸部X射线分类提供了一个强大的工具.
- 这种方法有助于机器学习在医学诊断方面的进步.
- 该技术显示出在帮助抗击COVID-19方面的巨大潜力.
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