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加强COVID-19检测:通过微调的EfficientNet深度学习架构优化性能.

Md Alamin Talukder1, Md Abu Layek1, Mohsin Kazi2

  • 1Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh.

Computers in biology and medicine
|December 2, 2023
PubMed
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Heliyon·2026

深度学习模型,特别是对胸部X射线进行微调的EfficientNetB4,在检测COVID-19和肺部疾病方面显示出高准确性. 这为医疗保健专业人员提供了快速而精确的诊断辅助.

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 放射学 放射学是一门学科.

背景情况:

  • 由于COVID-19的流行,需要快速准确地检测疾病.
  • 传统的COVID-19遗传检测是时间密集的,并且有局限性.
  • 放射性成像,特别是胸部X射线,为诊断提供了一个有希望的替代方案.

研究的目的:

  • 研究深度学习算法的有效性,应用于胸部X射线,以快速准确地检测COVID-19.
  • 通过微调转移学习模型来提高诊断准确度.

主要方法:

  • 通过对COVID-19X射线数据集 (2000张图像) 微调已建立的转移学习模型来利用深度学习.
  • 评估的模型包括Xception,InceptionResNetV2,ResNet50,ResNet50V2,EfficientNetB0和EfficientNetB4.4等,这些模型的使用情况有所不同.
  • 在单独的胸部X射线数据集 (4,350张图像) 上测试了EfficientNetB4,用于一般肺部疾病的识别.

主要成果:

  • 在各个模型中实现了高准确率,EfficientNetB4在COVID-19数据集上达到100%.
  • 微调的EfficientNetB4在肺部疾病检测方面表现出色 (准确率为99.17%,F1得分为99.14%).
关键词:
在 COVID-19 疫情中,深度学习是一种深度学习.诊断 诊断 诊断 诊断 诊断有效的网络.肺 肺 肺 肺 肺 肺 肺转移学习转移学习图像X射线图像X射线图像X射线图像X射线图像

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结论:

  • 精心调整的转移学习模型,特别是EfficientNetB4,对于使用X射线图像快速准确地检测COVID-19和肺部疾病非常有效.
  • 这种方法为放射科医生和医疗保健专业人员在患者诊断方面提供了宝贵的支持.