通过使用机器学习和深度学习通过X射线检测COVID-19,肺部不透明度和病毒性肺炎
Hajar Lamouadene1, Majid El Kassaoui2, Mourad El Yadari3
1Laboratory of Condensed Matter and Interdisciplinary Sciences, Physics Department, Faculty of Sciences, Mohammed V University in Rabat, Morocco.
Computers in biology and medicine
|April 8, 2025
概括
这项研究使用像CNN这样的深度学习模型,并在胸部X射线上转移学习,以准确诊断COVID-19和肺部疾病. 先进的模型达到高达99.20%的准确性,有助于早期检测.
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:
- 在COVID-19大流行期间,医疗保健系统面临重大压力.
- 早期诊断COVID-19和其他肺部疾病对于疾病制至关重要.
- 放射成像在诊断肺部疾病中起着至关重要的作用.
研究的目的:
- 开发和评估机器学习,深度学习和转移学习模型,用于自动化COVID-19和从X射线图像中诊断肺部疾病.
- 为了比较不同深度学习架构和优化器在分类肺部疾病中的性能.
- 评估人工智能在帮助医学专家快速准确检测疾病方面的潜力.
主要方法:
- 利用卷积神经网络 (CNN) 和支持矢量机 (SVM) 分类器在大量胸部X射线图像的数据集上.
- 应用转移学习技术,包括ResNet18,EfficientNet-CNN和Xception-CNN,与SVM相结合.
- 使用各种优化器评估模型性能,例如静态梯度下降 (SGD),Adam,RMSProp和Adagrad.
主要成果:
- 最初的CNN-SVM模型实现了86.18%的准确性.
- 转移学习模型表现出卓越的性能,ResNet18-SVM与SGD.实现高达98%的准确性.
- 效率网-CNN和Xception-CNN模型的分类准确率分别为99.20%和98.80%.
结论:
- 深度学习和转移学习技术显示出改善医疗图像诊断肺部疾病的准确性和效率的巨大潜力.
- 开发的模型可以作为有价值的工具,以支持医疗专业人员在临床决策.
- 建议对数据集多样性和高级增强进行进一步的研究,以提高模型的概括性.
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