深度学习解决方案对肺结节恶性疾病分类的性能:系统性审查
Hailun Liang1, Meili Hu2, Yuxin Ma1
1School of Public Administration and Policy, Renmin University of China, Beijing 100872, China.
Life (Basel, Switzerland)
|September 28, 2023
概括
深度学习方法显著提高了肺结节诊断的准确性. 与传统方法相比,这些先进的技术在分类肺结节恶性瘤方面提供了卓越的性能.
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
- 计算病理学计算病理学
背景情况:
- 计算机技术有助于肺结节的诊断.
- 深度学习通过消除手动特征提取,在图像处理中超越传统的机器学习,用于结节诊断.
- 深度学习方法在肺结节诊断中提供了更高的准确性.
研究的目的:
- 研究深度学习方法在分类肺结节恶性瘤中的有效性.
- 系统地审查和评估深度学习模型对肺结节恶性瘤分类的性能.
主要方法:
- 在PubMed和ISI Web of Science数据库中的系统文献搜索.
- 选择使用深度学习进行肺结节恶性瘤分类或预测的研究.
- 使用SAS版本9.4和Microsoft Excel 2010进行数据提取和分析.
主要成果:
- 分析包括了16项研究.
- 使用深度学习模型,包括卷积神经网络 (CNN),自动编码器 (AE) 和深度信念网络 (DBN).
- 深度学习模型表现出高性能,曲线下的面积 (AUC) 通常超过90%.
结论:
- 深度学习在肺结节的诊断和预测方面显示出明显的优势.
- 这项分析突出了肺结节检测深度学习的最新进展.
- 需要进一步的研究来解决与深度学习模型相关的缺点.
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