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用胸部X射线图像对肺部疾病进行准确诊断的机器学习算法的比较分析:对分段和非分段图像的平衡和不平衡数据的研究

Sreedevi Jasthy¹, Krishnamurthy Ramasubramanian², Radhakrishna Vangipuram³

¹Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Mahatma Gandhi Institute of Technology, Hyderabad, IND.

|March 4, 2024

概括

这项研究评估了机器学习,用于使用胸部X射线诊断肺部疾病. 随机森林和SVM显示出有希望的结果,特别是在不平衡数据的属性减少技术方面.

科学领域:

医疗成像医学成像
人工智能的人工智能
肺部病理学肺部病理学

背景情况:

肺部疾病导致全球显著的死亡率.
胸部X射线是一种具有成本效益的诊断成像工具.
准确和及时的诊断对于有效的治疗至关重要.

研究的目的:

通过使用胸部X射线图像来评估8个机器学习算法的肺部疾病诊断性能.
为了比较平衡和不平衡数据集的算法性能,以及细分与未细分的图像.
评估属性减少技术对诊断准确性的影响.

主要方法:

八个机器学习算法 (逻辑回归,天真贝叶斯,kNN,决策树,随机森林,SVM,Ridge,LASSO) 应用于胸部X射线数据集.
数据集包括平衡和不平衡的变化,有和没有图像细分.
使用主要组件分析 (PCA) 和线性差异分析 (LDA) 进行了属性减少.

主要成果:

在属性减少之前,决策树和随机森林在平衡数据上实现了74%的测试准确率;SVM实现了74%的测试准确率.
在PCA/LDA之后,决策树和随机森林保持了92%的训练准确率,而SVM保持了74%的测试准确率.
对于不平衡的数据,随机森林和SVM以80%的平衡准确度表现出最佳表现.

结论:

机器学习算法,特别是Random Forest和SVM,显示出通过胸部X射线准确诊断肺部疾病的潜力.
属性减少方法可以提高某些算法的性能.
建议进行进一步的研究以优化模型并探索额外的算法以提高诊断准确度.

关键词:

降低属性降低属性一个平衡的数据集.这就是CXR.肺部疾病肺部疾病机器学习是机器学习.

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