使用人工智能对肺结核进行两类分类的框架
Akansha Nayyar1, Rahul Shrivastava1, Shruti Jain2
1Department of BT & BI, JUIT, Solan, H.P., India.
Current medical imaging
|April 18, 2025
概括
机器学习模型通过胸部X射线 (CXR) 准确地识别结核病 (TB). 支持矢量机 (SVM) 实现了93.5%的准确性,显示AI.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 结核病 (TB) 是一个全球性的健康问题,需要早期发现.
- 通过胸部X射线 (CXR) 来诊断结核病对放射科医生来说可能是个挑战.
- 人工智能 (AI) 为自动化CXR分析提供了新的可能性.
研究的目的:
- 开发和评估机器学习 (ML) 模型,从CXR中自动检测结核病.
- 为了比较各种分类器的性能,包括SVM,后勤回归,决策树,kNN和ANN.
- 通过人工智能推进结核病诊断,可能导致更早的治疗和更好的患者结果.
主要方法:
- 使用了不同的参数,如边缘,形状和灰色水平差异统计 (GLDS).
- 在模型培训和验证中,雇员数据集的分割比率为70:30和80:20.
- 评估了用于自动结核病识别的多个ML分类器.
主要成果:
- 使用具有线性内核和70:30数据分割的支持矢量机 (SVM) 实现了93.5%的准确性.
- 与不同的特征提取技术,数据集分割和现有研究进行性能比较.
- 评估混合参数,以提高分类准确度.
结论:
- 人工智能显示出改善结核病检测率的巨大潜力.
- 该研究比较了各种ML模型,包括SVM,决策树,kNN,ANN和后勤回归.
- 人工智能驱动的结核病检测可以导致更早的诊断和改善疾病管理.
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