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胸部X射线异常的多标签分类使用转移学习技术

Jakub Kufel^1,2, Michał Bielówka³, Marcin Rojek³

¹Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland.

Journal of personalized medicine

|October 27, 2023

概括

这项研究引入了一种EfficientNet模型,用于从胸部X射线中分类14种疾病,达到84.28%的AUC-ROC得分. 这种深度学习方法有效地处理数据集失衡和多标签分类,以改进医学成像分析.

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:

深度神经网络已经彻底改变了图像分类.
卷积神经网络 (CNN) 技术越来越多地应用于医学成像.
胸部X射线分析对于诊断各种胸部疾病至关重要.

研究的目的:

用胸部X射线图像对14种不同的疾病进行分类.
开发一种超越现有的最先进方法的深度学习模型.
解决医学成像数据集中的挑战,如失衡和多标签分类.

主要方法:

使用EfficientNet模型架构进行特征提取.
使用自定义数据分割来管理数据集不平衡.
实现的二进制交叉损失用于多标签分类.
杆转移学习和深度学习工程技术.

主要成果:

在接收器运行特征曲线 (AUC-ROC) 下获得了84.28%的平均面积.
与以前的深度学习模型相比,拟议的解决方案表现出更高的性能.
该模型成功地从胸部X射线图像中分类了14种不同的疾病.
使用消费级图形处理单元 (GPU) 获得了有效的结果.

结论:

基于EfficientNet的方法为多标签胸部X射线分类提供了高度有效的解决方案.
转移学习和标准深度学习技术可以在可访问的硬件上实现高性能.
这项研究推进了深度学习在医学图像分析中用于疾病检测的应用.

关键词:

在美国,CNN是CNN.这是X射线.胸部X射线胸部X射线胸部X射线深度学习是一种深度学习.诊断分类诊断分类诊断分类机器学习是机器学习.放射学放射学是指放射学

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