RVCNet:一种混合深度神经网络框架,用于诊断肺部疾病
Fatema Binte Alam1, Prajoy Podder1, M Rubaiyat Hossain Mondal1
1Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.
PloS one
|December 28, 2023
概括
一个新的混合深度学习模型,RVCNet,准确地从X射线预测肺部疾病. 这种计算机辅助诊断系统 (CAD) 在改善早期肺部疾病检测和分类准确性方面显示出有前途.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 早期诊断肺部疾病对于降低死亡率至关重要.
- 计算机辅助诊断系统 (CAD) 帮助放射科医生准确诊断肺部疾病.
- 从放射图像中对多种肺部疾病进行分类需要进一步的研究.
研究的目的:
- 提出RVCNet,一种混合深度神经网络,用于从X射线图像中预测多类肺病.
- 通过使用深度学习技术来增强特征提取和分类.
主要方法:
- 开发了一个混合框架 (RVCNet),结合了ResNet101V2,VGG19和CNN模型.
- 采用超参数微调用于特征提取,并添加批量规范化,掉落和密集层用于分类.
- 使用了一组数据集,包括COVID-19的X射线图像,非COVID肺部感染,病毒性肺炎和正常病例 (2262次培训,252次测试图像).
主要成果:
- 通过Nadam优化器,RVCNet实现了91.27%的整体分类准确度.
- 性能指标包括AUC (92.31%),精度 (90.48%),回忆 (98.30%) 和F1评分 (94.23%).
- 在这个四类数据集上,RVCNet的表现优于其他独立模型,如ResNet101V2和VGG19.
结论:
- 从X射线图像上对多种肺部疾病进行分类,RVCNet表现出卓越的性能.
- 混合深度学习方法为计算机辅助肺部疾病诊断提供了一个有前途的工具.
- GRAD-CAM可视化有助于解释RVCNet的图像分类决定.
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