从使用卷积神经网络模型的胸部X射线图像对肺炎,结核和Covid-19进行分类
J Kiche1, Ivivi Mwaniki2, Idah Orowe1
1Mathematics Department, University of Nairobi,Kenya.
International journal of statistics and probability
|July 28, 2025
概括
这项研究开发了一种使用CNN的深度学习模型,以准确分类肺炎,结核病 (TB) 和COVID-19的胸部X射线. 该模型显示高精度,超过了诊断这些呼吸道疾病的传统方法.
科学领域:
- 医学成像分析 医学成像分析
- 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:
- 对肺炎,结核病 (TB) 和COVID-19等呼吸道疾病的准确诊断对于患者护理和公共卫生至关重要.
- 深度学习 (DL) 为医学图像分类和自动查提供了先进的功能.
研究的目的:
- 开发和评估一种基于DL的方法,将胸部X射线图像分为三个类别:肺炎,结核病和COVID-19.
- 评估DL模型与传统机器学习技术的性能.
主要方法:
- 利用卷积神经网络 (CNN) 来从胸部X射线图像中提取特征.
- 在大量注释的胸部X射线数据集上训练CNN模型.
- 使用精度,灵敏度,特异性和AUC-ROC评估性能,与SVM,决策树和XGBoost进行比较.
主要成果:
- 拟议的DL模型通过胸部X射线实现了对肺炎,结核病和COVID-19的高分类准确性.
- 与传统的机器学习方法相比,DL方法显示出更高的性能.
- 该模型的有效性在一个独立的测试集上得到了验证.
结论:
- DL模型显示了通过胸部X射线对常见的呼吸道感染进行准确和有效的分类的巨大潜力.
- 这种方法可以作为临床查工具,特别是在资源有限的环境中部署.
- 进一步验证和整合到临床工作流程是有必要的.
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