基于CT和X射线图像的COVID-19分类深度转移学习的新型大型实证研究
Mansour Almutaani1, Turki Turki2, Y-H Taguchi3
1Department of Computer Science, King Abdulaziz University, 21589, Jeddah, Saudi Arabia.
Scientific reports
|November 3, 2024
概括
这项研究开发了超过10,000个深度转移学习模型,用于使用AI检测COVID-19. 最好的模型在CT扫描上实现了高精度,改善了早期疾病预测.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 计算机辅助诊断 计算机辅助诊断
背景情况:
- 使用医学图像进行早期和准确的COVID-19预测对于及时诊断和疾病控制至关重要.
- 开发基于人工智能 (AI) 的模型对于提高诊断效率至关重要.
研究的目的:
- 探索用于COVID-19检测的深度转移学习 (DTL) 模型的大型模型空间.
- 为了确定卓越的DTL模型,以改善COVID-19诊断中的医疗图像分析.
主要方法:
- 处理了4481张来自Kaggle的CT和X射线图像,用于COVID-19和非COVID-19患者.
- 使用了四个预训练的深度学习模型 (ConvNeXt,EfficientNetV2,DenseNet121,ResNet34) 与结的特征提取层.
- 通过多数投票策略生成和组合多个DTL模型,创建超过10,000个模型.
主要成果:
- 最好的DTL模型HC在CT数据集上实现了0.909的曲线下的面积 (AUC).
- ConvNeXt模型在X射线数据集上显示了0.933的边际AUC,优于其他模型.
- 五次交叉验证证实了开发的DTL模型的有效性.
结论:
- 该研究成功生成并评估了大量用于COVID-19检测的DTL模型.
- 这些发现支持了"大一代模型"的方法,用于在医学诊断中推进人工智能.
- 开发的模型显示了通过医学图像分析加速COVID-19诊断的潜力.
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