用机器学习算法选择最佳特征的作用用于胸部X射线图像分析的机器学习算法
Mohini Manav1,2, Monika Goyal1, Anuj Kumar2
1Department of Physics, GLA University, Mathura, Uttar Pradesh, India.
Journal of medical physics
|August 14, 2023
概括
这项研究优化了从胸部X射线检测COVID-19的特征选择和分类. 粒子集群优化和支持向量机的组合实现了100%的准确性,有助于早期诊断.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机辅助诊断 计算机辅助诊断
背景情况:
- 准确和早期发现COVID-19对于患者管理至关重要.
- 胸部X射线 (CXR) 成像是一种广泛使用的诊断工具.
- 对CXR图像的自动分析可以帮助快速诊断COVID-19.
研究的目的:
- 将胸部X射线 (CXR) 图像分为COVID阳性和正常类别.
- 确定最佳特征和分类算法,以准确检测COVID-19.
- 为COVID-19开发一个计算机辅助诊断 (CAD) 系统.
主要方法:
- 使用对比度有限的适应性直方体平衡来增强CXR图像细节.
- 使用pyFeats提取的特征,包括基于统计,纹理和转换的方法.
- 采用以自然为灵感的算法 (灰狼优化,粒子群优化,遗传算法) 来进行特征选择和各种分类器 (随机森林,KNN,SVM,LightGBM) 来进行分类.
主要成果:
- 支持矢量机 (SVM) 分类器在所有特征选择方法中表现出卓越的性能.
- 粒子集群优化 (PSO) 为特征选择提供了最好的结果.
- SVM与PSO的组合实现了100%的准确性,精度,回忆和F1分数.
结论:
- 最佳的特征选择和分类器选择是CXR图像准确的计算机辅助诊断的关键.
- 建议的方法,使用最佳特征,可以作为放射科医生有价值的补充工具.
- 这种方法支持更早的疾病诊断和更知情的临床决策.
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