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通过混合视觉转换器和深度学习技术在CT肺血管扫描中改善肺栓塞检测

Abeer Abdelhamid¹, Amir El-Ghamry², Ehab H Abdelhay³

¹Electronics and Communications Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt. abeerabdo37@std.mans.edu.eg.

Scientific reports

|August 26, 2025

概括

这项研究引入了一种先进的深度学习组合模型,用于准确检测肺栓塞 (PE). 这一新框架显著改善了心血管保健的早期诊断和临床决策.

科学领域:

医学成像分析
心血管疾病
医疗保健中的人工智能

背景情况:

肺栓塞 (PE) 是一种严重的心血管疾病,也是导致死亡的主要原因.
精确的皮质前列腺检测对于患者的生存至关重要,
单个深度学习模型与复杂,多样化和不平衡的医学成像数据集作斗争.

研究的目的:

开发一个集体深度学习框架,以改善肺栓塞的分类.
利用卷积神经网络 (CNN) 和视觉转换器 (ViT) 的互补优势,提高准确性和稳定性.
为早期发育不良检测和临床决策提供一个强大的工具.

主要方法:

结合了ResNet50,DenseNet121和Swin变压器模型的整体框架被开发出来.
一个预处理管道包括基于自动编码器 (AE) 的维度减少,数据增强,离散波形转换 (DWT) 和Sobel过.
该模型在北美放射学协会 (RSNA-STR) 的PE数据集上进行了评估.

主要成果:

拟议的组合模型达到97.80%的准确性,接收器操作曲线下的面积 (AUROC) 为0.99.
与先前训练的最先进模型和基于视觉转换器 (ViT) 的方法相比,表现出更高的性能.
该方法有效地解决了复杂和不平衡的医学成像数据集的挑战.

结论:

开发的集体深度学习框架显著提高了肺栓塞检测的准确性和稳定性.
这种方法比医疗图像分析的现有单一模型深度学习方法提供了实质性的改进.
这些发现支持先进人工智能在改善早期勃起症诊断和帮助临床决策方面的潜力.

关键词:

计算机断层扫描 (CT)深度学习 (DL)离散波形变形 (DWT)肺栓塞 (PE) 的情况视觉变压器 (ViT)

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