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辐射融合-XR:使用机器学习进行COVID-19检测的混合LBP-HOG模型
1Department of Computer Science, PSGR Krishnammal College for Women, Coimbatore, Tamil Nadu, India.
Biotechnology and applied biochemistry
|July 11, 2025
概括
这项研究介绍了RadientFusion-XR,这是一种结合局部二进制模式 (LBP) 和面向梯度 (HOG) 组图特征的新型混合模型,用于在胸部X射线中准确检测COVID-19. 该模型实现了高精度,有助于早期诊断COVID-19和肺炎.
科学领域:
- 医学成像和人工智能 医学成像和人工智能
- 放射学和诊断成像 放射学和诊断成像
背景情况:
- 通过胸部X射线精确检测COVID-19对于及时的患者管理至关重要.
- 放射性图像分析由于COVID-19,正常和肺炎病例之间的重叠特征带来了挑战.
研究的目的:
- 研究将局部二进制模式 (LBP) 和面向梯度 (HOG) 的基因图组功能与机器学习相结合的有效性,以区分COVID-19与正常和肺炎胸部X射线.
- 引入和评估RadientFusion-XR混合融合模型,以提高诊断准确度.
主要方法:
- 开发RadientFusion-XR模型,一种混合融合方法,利用LBP和HOG功能与浅层学习算法.
- 该模型应用于胸部X射线图像,用于对COVID-19,正常和肺炎病例进行分类.
主要成果:
- 辐射融合-XR模型实现了特殊的准确性:二元分类 (COVID-19与正常) 99%和多类分类 (COVID-19,正常,肺炎) 97%的准确性.
- 混合特征提取和浅层学习方法显著提高了胸部X射线分析的诊断准确性.
结论:
- 该RadientFusion-XR模型展示了一种有希望和有效的工具,用于早期诊断COVID-19和肺炎使用胸部X射线.
- 该模型的可解释性和高性能使其成为临床应用和自动诊断系统的宝贵资产.
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