从使用CLAHE-YCrCb,LBP和机器学习算法的胸部X射线图像中检测COVID-19
Rukundo Prince1, Zhendong Niu2, Zahid Younas Khan3
1Department of Computer Science and Technology, Beijing Institute of Technology, Beijing, China.
BMC bioinformatics
|January 17, 2024
概括
这项研究引入了一种使用机器学习的高效方法,通过胸部X射线检测COVID-19. 这种方法提高了图像质量和提取特征,实现了高精度,降低了计算需求.
科学领域:
- 医疗成像医学成像
- 机器学习 机器学习
- 计算机辅助诊断 计算机辅助诊断
背景情况:
- COVID-19对全球健康构成重大威胁,需要快速准确的诊断工具.
- 现有的机器学习模型通过胸部X射线 (CXR) 来检测COVID-19面临限制,包括大量数据需求,高计算成本和长时间运行.
研究的目的:
- 开发一种使用CXR图像进行COVID-19检测的低成本和高效的计算方法.
- 克服现有的深度学习模型在数据,特征大小和计算资源方面的局限性.
主要方法:
- 用于CXR图像增强的有限对比度自适应式直方体均 (CLAHE).
- 利用照明-反射模型来估计从色差 (YCrCb颜色空间) 的反射.
- 使用反射率 (Cr) 和YCb的正常化局部二进制模式生成特征向量,然后使用像Naive Bayes这样的算法进行分类.
主要成果:
- 提出的方法在三个不同的数据集上实现了高准确率的99.01%,100%和98.46%.
- 在测试的分类算法中,Naive Bayes表现出了最强大的性能.
- 与最先进的方法相比,这种方法需要更少的手工制作功能和更少的计算能力.
结论:
- 开发的方法为COVID-19检测提供了具有成本效益和效率的解决方案,适合资源有限的环境.
- 编码材料和数据集的可访问性鼓励进一步的研究和开发.
- 这种原型可以帮助缺乏放射科医生的地区提高诊断能力.
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