MpoxNet:双分支深残留挤压和激发麻疹分类网络,具有注意力机制
Jingbo Sun1,2,3, Baoxi Yuan1,2,3, Zhaocheng Sun1
1School of Electronic Information, Xijing University, Xi'an, China.
Frontiers in cellular and infection microbiology
|June 24, 2024
概括
一个新的轻量级的人工智能模型,MpoxNet,提供快速而准确的麻疹分类. 这种高效的网络实现了高精度和回忆,优于现有的模型,计算复杂性降低.
科学领域:
- 医疗信息学 医疗信息学
- 计算机视觉 计算机视觉
- 传染病监测 传染病监测 传染病
背景情况:
- 病毒的出现是一个重大的全球卫生挑战,需要快速准确的诊断工具.
- 现有的分类模型可能缺乏及时应对流行病所需的效率和精度.
研究的目的:
- 开发一个高精度,轻量级的人工智能网络 (MpoxNet) 以快速分类麻疹.
- 通过先进的深度学习技术,提高天花检测的速度和安全性.
主要方法:
- 基于ConvNeXt架构的分类网络MpoxNet的设计.
- 实施一个两分支深度可分离的卷积残余挤压和激发模块,用于增强特征提取.
- 集成一个卷积注意力模块来完善关键特征识别.
主要成果:
- 在分类中,MpoxNet取得了95.28%的准确率,96.40%的精度,93.00%的回忆率和95.80%的F1-Score.
- 该模型与当前主流分类模型相比,表现明显优越.
- 由于MpoxNet的计算负担降低,FLOPS和参数数量分别为ConvNeXt-Tiny的30.68%和31.87%.
结论:
- MpoxNet提供了一种高效和有效的解决方案,用于麻疹疾病的分类.
- 由于MpoxNet的轻量级设计和高性能,它非常适合快速安全地检测全球流行病威胁.
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