从胸部X射线图像中联合诊断肺炎,COVID-19和结核病:一种深度学习方法
Mohammed Salih Ahmed1, Atta Rahman2, Faris AlGhamdi1
1Department of Computer Engineering, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia.
Diagnostics (Basel, Switzerland)
|August 12, 2023
概括
这项研究引入了一个深度学习模型,用于从胸部X射线中联合诊断肺炎,结核病和COVID-19. 该模型实现了高精度,证明了其改善肺部疾病检测的潜力.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 肺部病理学 肺部病理学
背景情况:
- 肺炎,COVID-19和结核病是流行和严重的肺部疾病.
- 现有的诊断方法往往侧重于单个疾病,忽视潜在的共同感染.
- 这些肺部疾病的同时出现需要综合诊断方法.
研究的目的:
- 从胸部X射线图像开发和评估深度学习模型,同时检测肺炎,COVID-19和结核病.
- 通过实现联合诊断来解决单一疾病诊断模型的局限性.
- 提高诊断多种常见肺部疾病的准确性和效率.
主要方法:
- 一个深度学习模型被设计用于分析胸部X射线图像.
- 该模型使用来自Kaggle的多个公共数据集进行了训练和验证.
- 使用精度,回忆和与最先进的方法进行比较来评估性能.
主要成果:
- 拟议的深度学习模型在所有疾病类别中实现了98.72%的整体准确性.
- 记录了特殊的召回得分:肺炎的99.66%,结核病的98.10%,以及COVID-19的96.27%.
- 该模型的表现优于现有的最先进的对未见数据的研究.
结论:
- 开发的深度学习模型有效地从胸部X射线进行肺炎,COVID-19和结核病的联合诊断.
- 该模型的高精度和回忆力表明它有可能成为临床环境中一个有价值的工具.
- 这种方法比传统的单一疾病诊断方法有了显著的进步.
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