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使用CycleGAN和转移学习的CT图像自动诊断COVID-19和转移学习

Navid Ghassemi^1,2, Afshin Shoeibi^1,2, Marjane Khodatars³

¹Faculty of Electrical Engineering, FPGA Lab, K. N. Toosi University of Technology, Tehran, Iran.

Applied soft computing

|June 22, 2023

概括

这项研究引入了一种使用深度神经网络和CycleGAN用于CT扫描的COVID-19诊断的自动化方法,达到99.60%的准确性. 一个新的,公开可用的COVID-19疑似病例数据集有助于开发可靠和可解释的诊断工具.

科学领域:

医疗成像医学成像
人工智能的人工智能
传染病诊断传染病诊断传染病诊断

背景情况:

准确的COVID-19诊断对于疫情控制至关重要.
计算机断层扫描 (CT) 成像是一种关键的诊断方式.
自动诊断工具可以帮助医疗保健专业人员.

研究的目的:

开发和评估使用CT成像检测COVID-19的自动深度学习方法.
通过先进的数据增强和可解释性技术,提高诊断准确性和可靠性.
引入一套新的,医生标记的CT图像数据集,这些图像来自可疑患有COVID-19的个人.

主要方法:

利用预训练的深度神经网络与循环生成对抗网络 (CycleGAN) 集成,用于数据增强.
开发了一个诊断模型,在一个新策划的数据集上实现高性能.
采用Grad-CAM用于模型解释性和可疑区域的识别.

主要成果:

在COVID-19检测中实现了99.60%的最先进的准确性.
使用来自189名患者的3163张CT图像数据集验证了该方法,其中包括一个新的对照组.
通过校准指标和视觉解释证明模型可靠性.

结论:

拟议的自动化方法通过CT扫描显示了COVID-19诊断的高准确性和可靠性.
新型数据集和可解释的AI方法有助于可靠的诊断工具.
这项研究支持将人工智能集成到医疗成像中,以应对流行病.

关键词:

在 COVID-19 疫情中,图像扫描 (CT) 扫描是一种扫描.循环GANAN是一个循环.深度学习是一种深度学习.转移学习转移学习

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