诊断COVID-19使用胸部X射线图像和疾病症状基于堆叠合奏深度学习
Abdulaziz AlMohimeed1, Hager Saleh2, Nora El-Rashidy3
1College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia.
Diagnostics (Basel, Switzerland)
|June 10, 2023
概括
人工智能 (AI) 使用症状和胸部X射线帮助早期检测COVID-19. 集成深度学习的堆叠组合模型在识别感染方面取得了卓越的性能.
科学领域:
- 计算智能和医疗信息学.
- 人工智能在医疗保健和疾病诊断中的应用.
背景情况:
- 冠状病毒疾病 (COVID-19) 由于其阴险的性质,提出了诊断挑战,通常在严重的肺损伤或血块形成后表现出来.
- 早期检测COVID-19对于有效的患者管理和疾病控制至关重要,但目前的方法面临局限性.
- 人工智能 (AI) 为提高COVID-19早期检测能力提供了有希望的途径.
研究的目的:
- 开发和评估用于早期检测COVID-19的先进AI驱动模型.
- 调查堆叠组合模型的有效性,利用患者症状数据和胸部X射线图像用于COVID-19识别.
- 与传统的机器学习和深度学习模型对比拟的堆叠组合模型的性能.
主要方法:
- 开发两个不同的堆叠组合模型,结合预训练的深度学习架构.
- 第一个模型整合了多层感知器 (MLP),循环神经网络 (RNN),长期短期记忆 (LSTM) 和门式循环单元 (GRU) 模型的输出,使用支持矢量机器 (SVM) 作为元学习器,在COVID-19症状数据集上进行训练.
- 第二个模型整合了VGG16,InceptionV3,Resnet50和DenseNet121模型的输出,也使用了SVM元学习器,训练了COVID-19胸部X射线图像数据集.
主要成果:
- 拟议的堆叠组合模型在评估COVID-19症状数据集时,与单个MLP,RNN,LSTM和GRU模型相比,表现优越.
- 同样,第二个拟议的堆叠组合模型在COVID-19胸部X射线图像数据集上表现优于其他深度学习模型.
- 两种拟议的模型在各自的数据集中实现了最高的性能指标,表明它们在COVID-19检测中的有效性.
结论:
- 堆叠组合模型,利用症状数据和胸部X射线图像,代表了早期和准确检测COVID-19的高效方法.
- 在堆叠框架中整合各种预训练模型显著提高了诊断性能.
- 人工智能驱动的诊断工具,特别是组合方法,对改善公共卫生对COVID-19等传染病的反应有很大的潜力.
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