论文标题
对COVID-19识别胸部CT图像识别的深度学习技术的评论
A review of Deep learning Techniques for COVID-19 identification on Chest CT images
论文作者
论文摘要
当前的Covid-19大流行是对人类直接影响肺部的严重威胁。对Covid-19的自动识别是卫生保健官员的挑战。用于诊断Covid-19的标准黄金方法是逆转录聚合酶链反应（RT-PCR），以从受影响的人那里收集拭子。收集拭子时遇到的一些限制与准确性和长期持续时间有关。胸部CT（计算机断层扫描）是另一种测试方法，可帮助医疗保健提供者迅速识别受感染的肺部区域。它被用作在早期阶段识别Covid-19的支持工具。借助深度学习，COVID-19的CT成像特征。研究人员已证明它对COVID-19 CT图像分类非常有效。在这项研究中，我们回顾了最近可以用来检测COVID-19疾病的深度学习技术。相关研究是由Web of Science，Google Scholar和PubMed等各种数据库收集的。最后，我们比较了不同深度学习模型的结果，并讨论了CT图像分析。
The current COVID-19 pandemic is a serious threat to humanity that directly affects the lungs. Automatic identification of COVID-19 is a challenge for health care officials. The standard gold method for diagnosing COVID-19 is Reverse Transcription Polymerase Chain Reaction (RT-PCR) to collect swabs from affected people. Some limitations encountered while collecting swabs are related to accuracy and longtime duration. Chest CT (Computed Tomography) is another test method that helps healthcare providers quickly identify the infected lung areas. It was used as a supporting tool for identifying COVID-19 in an earlier stage. With the help of deep learning, the CT imaging characteristics of COVID-19. Researchers have proven it to be highly effective for COVID-19 CT image classification. In this study, we review the recent deep learning techniques that can use to detect the COVID-19 disease. Relevant studies were collected by various databases such as Web of Science, Google Scholar, and PubMed. Finally, we compare the results of different deep learning models, and CT image analysis is discussed.
