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Ke Li, Yujiao Zhang, Jiaqiang Li

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 3, 2025

概括

一个新的轻量级AI模型在疫情期间快速诊断新出现的传染病 (EID). 这种高效的网络解决了资源的局限性,提高了关键公共卫生情况的准确性和速度.

科学领域:

人工智能的人工智能
医疗信息学医疗信息学
流行病学流行病学

背景情况:

新兴传染病 (EID) 导致患者数量激增,使医疗资源紧张,并阻碍及时诊断.
传统的EID诊断依赖于专业知识,冒着患者不适和医疗相关感染的风险,并且在流行病激增期间不足.
现有的EID自动诊断模型往往忽视了资源限制,只专注于准确性.

研究的目的:

为新兴传染病 (EID) 开发一种高性能,轻量级的诊断模型.
在流行病激增期间,在资源限制的情况下,应对快速EID诊断的挑战.
在疫情爆发期间提高诊断效率和患者的治疗结果.

主要方法:

引入了一种轻量级的神经网络,具有类似MobileViT的结构,交替使用MV2模块和一种新的MobileMamba块.
移动Mamba 块使用卷积来进行本地特征提取,并使用集团 SSM 模块来使用最小参数进行高效的全球特征提取.
评估了COVID-19和麻疹数据集上的模型.

主要成果:

在EID数据集上实现了最先进的性能,在精度,F1分数和AUC方面超过现有模型.
与ResNet 50,MobileViT和VMamba相比,提出的模型具有显著的轻量级,具有较少的参数和计算.
使用最小的计算资源证明了卓越的诊断能力.

结论:

开发的轻量级诊断模型为快速EID诊断提供了高性能解决方案.
这种方法有效地解决了在流行病激增期间需要有效的诊断工具的需求,克服了资源限制.
该模型显著促进及时诊断,可能改善患者的预后和疾病控制工作.

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