在COVID-19和糖尿病之间的相互作用中,探索胸部放射图像中的转移学习
Muhammad Shoaib1, Nasir Sayed2, Babar Shah3
1Department of Computer Science, CECOS University of IT and Emerging Sciences, Peshawar, Pakistan.
深度学习模型通过胸部X射线准确诊断COVID-19,显示出高灵敏度和特异性. 这种人工智能方法有助于检测病毒感染及其对肺部的影响.
科学领域:
- 医学成像和人工智能 医学成像和人工智能
- 传染病和内分泌学 传染病和内分泌学
背景情况:
- COVID-19和糖尿病之间的关系是复杂的,糖尿病患者的脆弱性增加,感染后糖尿病发展的可能性增加.
- 在COVID-19患者中,使用类固醇治疗咳症状也可能增加糖尿病风险,突出显示多方面的健康相互作用.
研究的目的:
- 实施和评估深度学习模型,以使用胸部X射线图像来诊断COVID-19感染.
- 在COVID-19检测中评估各种卷积神经网络架构的准确性,灵敏性和特异性.
主要方法:
- 收集了3000张胸部X射线图像,其中2440张用于训练四种深度学习模型:Inception-v3,ShuffleNet,Inception-ResNet-v2和NASNet-Large.
- 员工将学习转移到模型培训中,并在剩余的2080张图像上验证了绩效.
- 利用灵敏度,特异性,ROC曲线,精度回忆曲线,混矩阵和热图重建进行分析.
主要成果:
- 深度学习模型在通过胸部X射线检测COVID-19时实现了98%的高灵敏率和90%的特异性率.
- 定量分析和视觉表示 (ROC,混矩阵) 证实了模型的有效性.
- 自动热图重建准确地突出了可能受影响的肺部区域,与放射科医生的解释一致.
结论:
- 深度学习模型通过胸部X射线图显示了通过胸部X射线图进行准确和高效的COVID-19诊断的巨大潜力.
- 开发的方法为识别与病毒相关的肺部并发症提供了有价值的工具.
- 建议使用更大的数据集进行进一步的研究,以提高准确性和临床适用性.
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