从基于双SGAN模型的增强胸部X射线图像中检测肺炎
1College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China.
Scientific reports
|February 19, 2026
概括
这项研究引入了一种深度学习系统,用于在胸部X射线中检测肺炎,提高不平衡数据集的准确性. 新的双SGAN和ResNet18-SA模型在分类儿科肺部图像方面取得了高性能.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 肺炎检测的深度学习模型与不平衡的数据集作斗争,影响准确性.
- 从胸部X射线进行肺炎的准确分类对于及时诊断和治疗至关重要.
研究的目的:
- 开发一个强大的深度学习系统,使用胸部X射线图像检测肺炎.
- 解决阶级不平衡问题,提高肺炎分类模型的概括能力.
主要方法:
- 提出了一种新的双SGAN模型,包括光谱正常化,自我注意机制和链损失,用于稳定的训练和特征提取.
- 通过将空间注意力机制集成到ResNet18架构中,开发了一个ResNet18-SA分类模型.
- 利用肺炎MNIST数据集,包括儿科肺部图像,进行培训和评估.
主要成果:
- 与传统模型相比,ResNet18-SA模型表现出卓越的性能.
- 在肺炎分类中获得了高准确度 (95.83%),精度 (95.87%),回忆 (95.21%) 和F1评分 (95.52%).
结论:
- 拟议的深度学习系统有效地通过胸部X射线检测肺炎,即使数据集不平衡.
- 双SGAN和ResNet18-SA模型为肺炎诊断中的医学图像分析提供了更高的准确性和稳定性.
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