一种基于特殊功能的新型光谱转换技术,用于改进胸部X射线图像分类
1Department of Computer Science and Informatics, Applied College, Taibah University, Medina, Saudi Arabia.
PloS one
|June 11, 2025
概括
这项研究引入了一种新的胸部X射线分类方法,使用从Legendre多项式派生的光谱系数. 这种方法显著提高了机器学习分类器的准确性,如支持向量机和随机森林用于诊断正常,COVID-19和肺炎病例.
科学领域:
- 医学成像分析 医学成像分析
- 医疗保健中的机器学习
- 信号处理 信号处理
背景情况:
- 胸部X射线分类对于医学诊断至关重要.
- 机器学习具有先进的诊断能力.
- 将数据转换为缩小的特征空间在分类中很常见.
研究的目的:
- 提出一种使用光谱系数的新胸部X射线图像分类方法.
- 开发一种用于将医疗图像转换为光谱空间的机制.
- 为了评估光谱时刻在分类X射线图像不同患者的条件的有效性.
主要方法:
- 开发了一种方法来将医疗图像转换为光谱空间,使用Legendre类型的光滑多项式.
- 在Legendre多项式空间中计算了光谱时刻.
- 训练支持矢量机 (SVM) 和随机森林分类器使用这些光谱时刻在正常,COVID-19和肺炎X射线图像的数据集上.
- 使用Matlab进行模拟,用于图像预处理和光谱时刻生成,以及Python用于分类器实现.
主要成果:
- 建议的光谱时刻,当与SVM和随机森林一起使用时,显著提高了分类准确性.
- 在分类胸部X射线图像中达到0.975的高精度.
- 通过参数研究证明了基于光谱时刻的方法的效率和准确性.
结论:
- 开发的基于光谱系数的方法为准确的胸部X射线图像分类提供了有希望的方法.
- 该方法显示了令人满意的结果,特别是在正常,COVID-19和肺炎病例之间进行区分.
- 需要进一步的研究来开发一种更准确,更快速的版本的这种方法.
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