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Gelan Ayana1,2,3, Beshatu Debela Wako4, So-Yun Park1
1Department of Biomedical Engineering, Kumoh National Institute of Technology, Gumi 39253, Republic of Korea.
Diagnostics (Basel, Switzerland)
|July 12, 2025
概括
与CNN相比,视觉变压器 (ViTs) 在 (Mpox) 检测方面表现出更高的准确性. 这种人工智能方法为早期Mpox诊断提供了一个可扩展,可靠的工具,特别是在资源有限的环境中.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 病毒学 病毒学
背景情况:
- 全球 (Mpox) 疫情需要快速准确的诊断工具.
- 像PCR这样的传统方法是资源密集型的,推动了对替代方法的需求.
- 皮肤病变图像分析为自动Mpox检测提供了一个有希望的途径.
研究的目的:
- 评估视觉转换器 (ViT) 的有效性,用于使用病变图像自动检测麻疹.
- 将基于ViT的转移学习 (TL) 模型与基于CNN的TL和从头开始训练的ViT模型的性能进行比较.
主要方法:
- 在Mpox损伤图像数据集上微调预训练的ViT模型,以创建TL模型.
- 使用准确度,精度,回忆,F1得分和AUC来评估模型性能.
- 使用可转移性测量和Grad-CAM用于可解释性和特征分析.
主要成果:
- ViT模型的表现明显优于CNN模型,AUC为0.948,准确度为0.942 (p<0.05).
- ViT模型显示出优异的可转移性,有效地将学习权重转移到Mpox图像中.
- Grad-CAM可视化证实了ViT专注于临床相关特征,支持诊断可靠性.
结论:
- 基于ViT的自动检测为Mpox诊断提供了更高的准确性.
- 这种人工智能方法对于在资源有限的现场环境中早期检测Mpox是一种有价值的工具.
- 这些发现支持更快的疫情应对和改善公共卫生资源分配.
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