基于计算机的定量图像纹理分析,使用胸部X射线图像中的多线性诊断
Antonio Quintero-Rincón1,2, Ricardo Di-Pasquale1, Karina Quintero-Rodríguez3
1Department of Data Science, Data Science and AI Laboratory, Catholic University of Argentina (UCA), Buenos Aires, Argentina.
这项研究引入了一种新的算法,用于检测异常的X射线图像,使用奇数值分解 (SVD) 纹理特征. 一个独特的"调节重量"参数显著提高了对COVID-19等疾病的分类准确性.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 生物医学工程 生物医学工程
背景情况:
- 图像纹理分析仍然是医学诊断的一个挑战.
- 在X射线图像中自动检测异常对于及时诊断至关重要.
研究的目的:
- 开发一种自动化工具,使用组织衰减检测异常X射线图像.
- 为了提高各种肺部疾病的X射线图像分类的准确性.
主要方法:
- 使用单数值分解 (SVD) 来提取纹理特征 (单数值,条件索引).
- 引入了一个"调重量"参数,该参数来源于SVD变异的统计分析,以计算组织衰减变异性.
- 实施了集体袋装树木分类模型,用于分类胸部X射线图像.
主要成果:
- 在没有调节重量的情况下达到88%的准确性,在应用调节重量时达到99%的准确性.
- 在标准性能指标 (如准确性,AUC,真正阳性率) 中显著改善.
- 在一个不平衡的胸部X射线数据集上表现优于现有的最先进的方法.
结论:
- 提出的基于SVD的方法具有调节重量参数,对于自动化X射线图像分析非常有效.
- 调整重量参数显著提高了分类准确性,并减少了错误分类错误.
- 这种方法为通过胸部X射线诊断各种肺部病理提供了强大的解决方案.
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