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基于机器学习的方法从乳房图片估计乳腺密度:一个全面的审查.

Khaldoon Alhusari1, Salam Dhou1

  • 1Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates.

Journal of imaging
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概括
此摘要是机器生成的。

精确的乳腺密度估计从乳房影像对于早期发现乳腺癌至关重要. 机器学习,特别是像CNN这样的深度学习模型,显示出希望,但在主观性和成本方面面临挑战.

关键词:
乳腺癌 乳腺癌 乳腺癌乳腺密度 乳腺密度机器学习是机器学习.哺乳镜密度估计 哺乳镜密度估计

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科学领域:

  • 放射学和医学成像学 医学成像学
  • 在瘤学瘤学.
  • 医疗保健中的人工智能

背景情况:

  • 乳腺癌是女性最常见的癌症,乳腺密度是一个重要的风险因素.
  • 高乳腺密度可以掩盖乳房图片上的瘤,降低检测灵敏度.
  • 准确的乳腺密度评估对于风险分层和早期诊断至关重要.

研究的目的:

  • 进行一项全面的审查乳房图密度估计技术.
  • 专注于基于机器学习的方法来评估乳腺密度.
  • 确定当前的局限性,并建议未来的研究方向.

主要方法:

  • 对基于视觉,软件,机器学习和细分的方法进行审查,以估计乳腺扫描密度.
  • 机器学习方法的分类为传统和深度学习方法.
  • 分析常用的模型,如支持向量机 (SVM) 和卷积神经网络 (CNN).

主要成果:

  • 机器学习模型,特别是SVM和CNN,可以达到很高的分类准确度 (76.70%98.75%).
  • 目前的方法受到主观性和成本效益低下的限制.
  • 深度学习模型在乳房图密度估计方面显示出显著的潜力.

结论:

  • 机器学习为客观和高效的乳房扫描密度估计提供了强大的工具.
  • 未来的研究应该解决主观性和成本障碍,探索无监督细分和像变压器这样的先进模型.
  • 改进的乳腺密度估计方法可以提高乳腺癌的早期检测和诊断.