从胸部X射线图像中检测肺炎,使用深度学习和对不平衡数据集的转移学习
Faisal Alshanketi1, Abdulrahman Alharbi1,2, Mathew Kuruvilla2
1Department of Computer Science, College of Engineering and Computer Science, Jazan University, 45142, Jazan, Saudi Arabia.
Journal of imaging informatics in medicine
|November 18, 2024
概括
深度学习模型有效地通过X射线检测肺炎,转移学习和数据平衡提高了不平衡数据集的准确性. 半监督方法显示了在肺炎诊断中利用未标记数据的前景.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机辅助诊断 计算机辅助诊断
背景情况:
- 肺炎是全球主要的健康问题,需要及时诊断.
- 深度学习可以通过胸部X射线自动检测肺炎.
- 不平衡的数据集在开发强大的AI模型方面构成了重大挑战.
研究的目的:
- 研究用于肺炎检测的深度学习,专注于不平衡的数据集.
- 评估各种深度学习架构 (VGG,ResNet,ViT) 和缓解策略.
- 探索转移学习,零射击,少数射击和半监督学习以提高绩效.
主要方法:
- 在胸部X射线,BRAX和CheXpert数据集上评估VGG,ResNet和ViT架构.
- 应用来自ImageNet的转移学习和数据增强技术.
- 实施半监督学习 (平均教师) 和平衡权重策略.
主要成果:
- 转移学习,数据增强和平衡权重显著改善了不平衡数据集的性能.
- 深度学习模型在肺炎检测方面表现出高准确度.
- 半监督学习有效利用未标记的数据,增强诊断能力.
结论:
- 深度学习,特别是转移学习和数据平衡,显示了准确检测肺炎的巨大潜力.
- 战略选择应根据数据集特征进行量身定制.
- 半监督学习为改进人工智能驱动的医学诊断提供了一个有希望的途径.
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