COVID-SegNet:基于编码解码器的架构,用于胸部X射线中COVID-19病变细分的X射线
Tarun Agrawal1, Prakash Choudhary2
1Department of Computer Science and Engineering, National Institute of Technology Hamirpur, Hamirpur, Himachal Pradesh 177005 India.
Multimedia systems
|June 26, 2023
概括
这项研究引入了一个基于UNet的模型,用于在胸部X射线中对COVID-19病变进行细分,从而提高诊断准确度. 增强型模型的性能优于现有方法,帮助医学专家识别肺部感染.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机辅助诊断 计算机辅助诊断
背景情况:
- 随着COVID-19的流行,医疗保健系统已经被压倒,需要先进的诊断工具.
- 目前的COVID-19计算机辅助诊断模型往往缺乏胸部X射线中感染区域的精确定位.
- 精确细分肺病变对于有效的医学诊断和治疗计划至关重要.
研究的目的:
- 提出一种基于UNet的新型编码解码器架构,用于胸部X射线中精确的COVID-19病变细分.
- 通过使用注意力机制和的空间金字塔聚合模块来增强细分模型的性能.
- 评估拟议模型与COVID-19检测的最先进方法的有效性.
主要方法:
- 开发基于UNet的编码器-解码器网络,其中包含注意力机制.
- 集成基于卷积的形空间金字塔聚合模块,以改进特征提取.
- 在胸部X射线数据集上使用子相似系数和雅卡德指数指标进行性能评估.
主要成果:
- 拟议的模型实现了0.8325的子相似系数和0.7132.7的贾卡德指数.
- 与标准UNet模型相比,该模型在COVID-19病变细分方面表现出卓越的性能.
- 一项废除研究证实了注意力机制的显著贡献,以及 atrial空间金字塔聚合模块中的特定扩张率.
结论:
- 开发的基于UNet的模型有效地在胸部X射线中对COVID-19病变进行细分,从而提高了诊断精度.
- 整合注意力机制和的空间金字塔聚合提高了细分的准确性.
- 这种方法为医学专家在诊断和管理COVID-19方面提供了宝贵的工具.
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