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计算机辅助诊断系统:对经典机器学习与基于深度学习的方法进行比较研究.

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  • 1SERCOM Laboratory, Polytechnic School of Tunisia, University of Carthage, PO Box 743, La Marsa, 2078 Tunisia.

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机器学习 (ML) 模型在医疗保健中提供了更好的诊断准确性,有助于瘤分类和COVID-19检测. 经典和深度学习方法之间的选择取决于最佳患者结果的数据集大小.

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科学领域:

  • 医学成像分析 医学成像分析
  • 医疗保健中的人工智能
  • 诊断系统 诊断系统

背景情况:

  • 准确的诊断对于患者的治疗结果至关重要,但临床判断的变化可能会导致低于最佳的或致命的治疗.
  • 机器学习 (ML) 提供自动化数据分析,以创建预测模型,优化诊断并可能挽救生命.

研究的目的:

  • 审查和评估用于瘤分类和COVID-19感染检测的不同机器学习模型.
  • 将经典的计算机辅助诊断 (CAD) 系统与基于深度学习的CAD系统进行比较.

主要方法:

  • 审查用于医疗图像分析的各种机器学习模型和算法,用于分类任务.
  • 基于手动或单独的ML特征提取的经典CAD系统与具有自动特征识别和提取的深度学习CAD系统的比较.

主要成果:

  • 基于经典和深度学习的CAD系统在诊断任务中表现相似.
  • 选择合适的CAD系统取决于数据集.

结论:

  • 在医疗图像分析中,手动特征提取对于小型数据集是首选的.
  • 深度学习方法更适合较大的数据集,提供自动特征提取以提高诊断准确度.