一个新的基于深度学习的COVID-19分类框架,辅助加权平均组合建模
Gouri Shankar Chakraborty1, Salil Batra1, Aman Singh2,3,4
1Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, Punjab, India.
Diagnostics (Basel, Switzerland)
|May 27, 2023
概括
这项研究引入了一套集体深度学习模型,用于从医学图像中准确检测COVID-19. 权重平均组合技术实现了高精度,为传统方法提供了可靠的自动化替代方案.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 传染病的诊断 感染性疾病的诊断
背景情况:
- 由SARS-CoV-2引起的COVID-19呈现出各种症状,需要及时诊断以预防严重的肺部并发症.
- 目前的诊断方法,如RT-PCR是准确的,但耗时和劳动密集型.
- 医疗成像的深度学习提供了自动化的COVID-19检测,但现有的系统面临着诸如过拟合和泛化错误等局限性.
研究的目的:
- 开发一种高精度,高效和可靠的基于深度学习的技术来检测COVID-19.
- 通过使用转移学习和改进的预处理来解决现有自动化系统的局限性.
- 通过综合方法提高COVID-19检测的可靠性.
主要方法:
- 通过结合Xception,VGG19和ResNet50V2卷积神经网络 (CNN) 模型,创建了一个集体深度学习模型.
- 使用了加权平均整体 (WAE) 预测策略.
- 转移学习和增强的预处理技术应用于两个基准数据集.
主要成果:
- 在WAE模型实现了97.25%的准确性二进制分类和94.10%的多类分类COVID-19的准确性.
- 与单个CNN模型相比,提出的方法显示了更高的准确性.
- 使用转移学习和高级预处理提高了检测系统的可靠性.
结论:
- 拟议的集体深度学习技术为自动检测COVID-19提供了可靠和准确的方法.
- 这种方法克服了传统方法和现有的深度学习模型的局限性.
- 这项研究强调了合体学习和转移学习在医疗图像分析中用于疾病诊断的潜力.
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