论文标题
使用CNN和VGG16，在胸部CT图像上的COVID-19疾病鉴定
COVID-19 Disease Identification on Chest-CT images using CNN and VGG16
论文作者
论文摘要
一种名为Covid-19的新发现的冠状病毒疾病主要影响人类呼吸系统。 Covid-19是一种由源自中国武汉的病毒引起的传染病。早期诊断是医疗保健提供者的主要挑战。在较早的阶段，医疗机构令人眼花azz乱，因为没有适当的健康辅助工具或医学可以检测到COVID-19。引入了一种新的诊断工具RT-PCR（逆转录聚合酶链反应）。它从患者的鼻子或喉咙中收集拭子标本，在那里共有19个病毒。该方法有一些与准确性和测试时间有关的局限性。医学专家提出了一种称为CT（计算机断层扫描）的替代方法，该方法可以快速诊断受感染的肺部区域并在早期阶段识别Covid-19。使用胸部CT图像，计算机研究人员开发了几种识别Covid-19疾病的深度学习模型。这项研究提出了卷积神经网络（CNN）和基于VGG16的基于VGG16在胸部CT图像上自动化的COVID-19鉴定的模型。使用14320 CT图像的公共数据集的实验结果显示，CNN和VGG16的分类精度分别为96.34％和96.99％。
A newly identified coronavirus disease called COVID-19 mainly affects the human respiratory system. COVID-19 is an infectious disease caused by a virus originating in Wuhan, China, in December 2019. Early diagnosis is the primary challenge of health care providers. In the earlier stage, medical organizations were dazzled because there were no proper health aids or medicine to detect a COVID-19. A new diagnostic tool RT-PCR (Reverse Transcription Polymerase Chain Reaction), was introduced. It collects swab specimens from the patient's nose or throat, where the COVID-19 virus gathers. This method has some limitations related to accuracy and testing time. Medical experts suggest an alternative approach called CT (Computed Tomography) that can quickly diagnose the infected lung areas and identify the COVID-19 in an earlier stage. Using chest CT images, computer researchers developed several deep learning models identifying the COVID-19 disease. This study presents a Convolutional Neural Network (CNN) and VGG16-based model for automated COVID-19 identification on chest CT images. The experimental results using a public dataset of 14320 CT images showed a classification accuracy of 96.34% and 96.99% for CNN and VGG16, respectively.
