集成卷积和变压器网络,用于精确诊断流域和出血性中风
Tanzeela Kousar1, Mohd Shafry Mohd Rahim2, Mohammed Alsuhaibani3
1Faculty of Computing , Universiti Teknologi Malaysia , Johor Bahru, Malaysia. Tanzeela@graduate.utm.my.
Scientific reports
|November 25, 2025
概括
一个新的AI模型准确地检测和分类缺血流域中风 (IWS),经常错过当前的方法. 这一进步改善了早期中风诊断和临床决策.
科学领域:
- 神经学 神经学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 缺血流域中风 (IWS) 经常被忽视,因为其表现微妙,尺寸小,使诊断复杂化.
- 现有的诊断和机器学习方法与IWS检测和分类扎,在中风管理中造成差距.
- 目前的方法往往专注于更常见的中风类型,忽视了IWS带来的具体挑战.
研究的目的:
- 通过CNN-变压器融合模型引入一种用于IWS识别,细分和分类的新方法.
- 评估拟议方法在DWI MRI图像上对IWS和出血中风数据集的有效性.
- 解决机器学习模型在区分中风大小和亚型方面的局限性.
主要方法:
- 开发CT-Transfusion,一种CNN-变压器融合模型,其中包含一个特征融合模块 (FFM).
- 该FFM整合了地方和全球特征通过调整空间定向和统一特征在补丁层面.
- 一个CNN解码器重建了最终的图像,对DWI MRI和PhysioNet出血中风数据集进行了评估.
主要成果:
- 拟议的模型实现了94.79%的准确性,93.0%的精度,95.0%的回忆率和94.0%的F1-Score用于IWS检测和分类.
- 在IWS上表现优于修改移动网络-UNet,高效网络-Unet,DeepLabV3Plus,ResNet50和InceptionV3等基准模型.
- 在出血中风数据上取得了卓越的性能,准确率为99.7%,精度为99.8%,回忆率为99.8%,F1-Score为99.8%.
结论:
- 双流CNN-变压器方法显著提高了对中风评估的诊断准确性,而不需要多式成像.
- 该模型在临床环境中证明了其实用性,改善了早期中风检测和帮助临床决策.
- 集成到自动化图像分析工作流程的潜力,为中风评估提供可扩展的解决方案.
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