一个多阶段的框架,用于检测呼吸系统疾病,并评估胸部X射线图像中的严重程度
Pranab Sahoo1, Saksham Kumar Sharma2, Sriparna Saha3
1Department of Computer Science & Engineering, Indian Institute of Technology Patna, Patna, 801106, India. pranab_2021cs25@iitp.ac.in.
这项研究引入了胸部X射线分析的新型多阶段框架,改善了肺部疾病诊断和COVID-19严重程度评估. 该系统增强了分类和定位,帮助放射科医生,减少工作量.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 胸部X射线图对于诊断肺部疾病如肺炎,结核病和COVID-19至关重要.
- 当前的计算机辅助诊断 (CAD) 系统往往缺乏精确的定位和严重性评估能力.
- 使用突出度地图的现有方法只提供异常的近似定位.
研究的目的:
- 为增强胸部X射线分析开发一个全面的多阶段框架.
- 改善肺部疾病,特别是COVID-19的局部化和严重程度评估.
- 为临床医生和放射科医生提供一个有价值的工具,以帮助诊断和减少报告时间.
主要方法:
- 一个多阶段的框架,包括肺部区域的识别和过外部区域.
- 一种基于模糊组合的高级图像分类方法.
- 对COVID-19病例的感染识别和量化,包括使用修改后的RALE系统进行严重性评分 (0-3).
主要成果:
- 拟议的框架成功地确定了相关的肺部区域,并将图像分类为特定的类别.
- COVID-19病例被准确地分为严重程度 (轻度,中度,严重,危急).
- 该系统超越了公开可用的数据集上的先前最先进的结果,证明了通过肺部细分进行增强的分类.
结论:
- 开发的框架为肺部疾病和COVID-19评估的胸部X射线分析提供了重大进展.
- 它作为放射科医生有价值的二级阅读器,提高了诊断准确性和效率.
- 该解决方案有可能减少报告的处理时间,并减轻医疗保健专业人员的工作量.
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