德萨COVID-19:深度突出COVID-19基于图像的质量评估
Risnandar1,2
1The Intelligent Systems Research Group, School of Computing, Telkom University, Jl. Telekomunikasi No. 1, Terusan Buahbatu-Dayeuhkolot, Bandung, West Java 40257 Indonesia.
概括
一种新方法DeSa COVID-19使用深层卷积神经网络进行先进的COVID-19图像质量评估. 它显著优于现有的胸部X射线和CT数据集上的全参考医学图像质量评估技术.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 图像质量评估 图像质量评估
背景情况:
- 准确评估COVID-19医疗图像对于诊断和治疗至关重要.
- 现有的全参考图像质量评估 (FR-IQA) 方法可能无法完全捕捉医疗图像质量的细微差别.
- 深度卷积神经网络 (DCNNs) 在图像分析任务中表现有前途.
研究的目的:
- 开发一种先进的方法来评估COVID-19图像质量.
- 将突出的COVID-19图像地图与DCNN结合起来,以增强FR-IQA.
- 将拟议方法的性能与现有的FR-IQA技术进行比较.
主要方法:
- 开发了DeSa COVID-19,一个DCNN模型,包含一个突出的COVID-19图像地图.
- 采用n-凸方法进行全参考图像质量评估 (FR-IQA).
- 评估了COVID-chestxray和COVID-CT数据集的性能,包括扭曲和未扭曲的图像.
主要成果:
- 德萨COVID-19在COVID-chestxray和COVID-CT数据集上表现出了显著的成就.
- 拟议的DCNN和DeSa COVID-19方法超过了其他先进的FR-IQA技术.
- 在Spearman的等级顺序相关系数 (SROCC) 和线性相关系数 (LCC) 测量中,DeSa COVID-19取得了卓越的表现,明显优于推的DCNN和其他先进的FR-MIQA方法.
- 通过优化函数操作来降低计算复杂性.
结论:
- 德萨COVID-19方法为COVID-19图像质量评估提供了一种优越的方法.
- 在医疗图像质量评估中,DCNN架构提供了增强的性能.
- 该研究强调了先进的人工智能模型在改善诊断成像质量的潜力.
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