TL-PneuNet:一个基于转移学习的肺炎分类框架
Biswajit Tripathy1, Shakir Khan2, Sujit Bebortta1
1Department of Computer Science, Ravenshaw University, Cuttack, 753003, India.
Scientific reports
|November 17, 2025
概括
转移学习 (TL) 改善了从胸部X射线检测的肺炎检测. ResNet152V2模型实现了83.17%的准确性,帮助医疗保健专业人员快速诊断.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 呼吸系统医学 呼吸系统医学
背景情况:
- 肺炎是一种严重的肺部感染,会导致气泡中积聚液体,如果诊断延迟,可能危及生命.
- 目前使用有限辐射水平进行胸部X射线的诊断方法可能导致肺炎检测不可靠.
- 转移学习 (TL) 是一种有希望的方法,可以提高肺炎诊断的准确性和效率.
研究的目的:
- 开发和评估转移学习 (TL) 模型,以使用胸部X射线图像准确预测肺炎.
- 在TL框架内对不同视觉模型 (Xception,VGG16,ResNet152V2) 的性能进行比较,用于肺炎分类.
- 评估TL在协助肺病学家和医生快速准确地诊断肺炎方面的潜力.
主要方法:
- 利用5856张高度不平衡的胸部X射线图像的数据集进行模型训练和评估.
- 应用转移学习 (TL) 技术适应预先训练的视觉模型,包括Xception,VGG16和ResNet152V2.2.
- 在胸部X射线数据集上训练和微调选定的深度学习模型,以区分正常和肺炎病例.
主要成果:
- 在胸部X射线数据集上,TL模型表现出强的性能,精度为80.45% (Xception),80.77% (VGG16) 和83.17% (ResNet152V2).
- 与Xception和VGG16相比,ResNet152V2表现出优越的性能,实现了最高的精度.
- 在肺炎分类方面,ResNet152V2模型获得了79.87%的精度得分和97.69%的回忆得分.
结论:
- 提议的TL框架有效地从胸部X射线图像中对肺炎进行分类,突出了医学诊断中深度学习的潜力.
- 当与TL一起使用ResNet152V2时,它在识别肺炎方面表现出显著的有效性,为临床决策支持提供了可靠的工具.
- 这种方法可以使医疗保健专业人员能够实现更快,更准确的诊断,从而有可能改善肺炎病例患者的治疗结果.
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