从机器学习到组合方法:对乳腺造影分类方法的系统审查
Hanifah Rahmi Fajrin1,2, Se Dong Min1,3
1Department of Software Convergence, Soon Chun Hyang University, Asan 31538, Republic of Korea.
Diagnostics (Basel, Switzerland)
|November 27, 2025
概括
机器学习和深度学习模型在乳腺癌分类中显示出高精度,但混合模型为多类检测提供了更高的稳定性和效率. 这些进展对于改善早期诊断和患者的治疗结果至关重要.
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
- 计算生物学 计算生物学
背景情况:
- 乳腺癌是妇女死亡的主要原因,强调需要改进诊断工具.
- 早期检测和准确的分类对于提高治疗疗效和患者生存率至关重要.
研究的目的:
- 审查和比较机器学习 (ML),深度学习 (DL) 和混合/整体模型的乳腺癌分类使用乳房影像.
- 评估计算机辅助诊断中不同AI方法的性能,优势和局限性.
主要方法:
- 按照PRISMA指南进行系统的文献搜索,包括2018-2025年间的50项研究.
- 基于乳腺造影数据集的模型分析,专注于预处理,特征提取,优化和分类性能.
- 对ML,DL和混合模型架构进行比较评估.
主要成果:
- 机器学习 (ELM) 和深度学习 (Vision Transformers) 在二进制分类任务中实现了100%的准确性.
- 像IEUNet++这样的混合模型显示出高精度 (99.87%) 和强大的多类分类能力.
- 与混合方法不同,ML和DL模型通常需要大量的预处理和特征工程.
结论:
- 混合型号为多类乳腺癌分类提供了高精度,强度和效率的有希望的平衡.
- 未来的研究应该专注于开发结合临床应用的准确性,可解释性和资源效率的人工智能解决方案.
- 人工智能驱动的分类系统的进步对于支持早期乳腺癌检测和改善患者治疗结果至关重要.
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