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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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通过混合功能和机器学习超参数优化技术来增强肺癌检测.

Liangyu Li1,2, Jing Yang3, Lip Yee Por3

  • 1Center for Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia.

Heliyon
|February 26, 2024
PubMed
概括

这项研究使用混合机器学习方法提高了肺癌检测. 结合灰级共发生矩阵 (GLCM) 和自编码器特征,可显著提高早期肺癌识别的诊断准确性.

关键词:
自动编码器和灰色水平共发生 (GLCM)分类 分类 分类 分类.哈拉利克纹理的特点是哈拉利克纹理的特点.肺癌类型 肺癌类型

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 在瘤学瘤学.

背景情况:

  • 早期肺癌检测对于改善患者存活率至关重要.
  • 精确的特征提取是用于医学诊断的机器学习的一个关键挑战.
  • 结合相关特征可以显著提高诊断模型的性能.

研究的目的:

  • 开发和评估用于肺癌检测的混合特征提取方法.
  • 评估将灰色级共发生矩阵 (GLCM) 与自动编码器特性集成的有效性.
  • 提高监督机器学习模型在识别肺癌方面的准确性.

主要方法:

  • 开发了一种混合特征提取方法,将GLCM与Haralick特征和自动编码器特征结合起来.
  • 这些集成功能被用作监督机器学习算法的输入.
  • 支持向量机 (SVM) 模型,包括辐射基函数 (RBF),高斯式和多项式内核,用于分类.

主要成果:

  • 集成GLCM,Haralick和自动编码器特征的混合方法实现了高精度的SVM多项式 (99.89%).
  • 在SVM Gaussian和SVM RBF中,SVM Gaussian和SVM RBF使用组合的功能集展示了完美的性能指标.
  • 使用GLCM与Haralick功能,SVM高斯式实现了99.56%的准确率,SVMRBF使用GLCM与Haralick功能实现了99.35%的准确率.

结论:

  • 拟议的混合特征提取方法显示了提高肺癌检测精度的巨大潜力.
  • 这种方法可以有助于开发用于肺癌诊断和治疗计划的改进系统.
  • 机器学习,特别是高级功能集成,为推进瘤诊断提供了一个有前途的途径.