论文标题
海报：通过转移学习技术在CT扫描上诊断Covid-19的诊断：深度学习模型的比较
POSTER: Diagnosis of COVID-19 through Transfer Learning Techniques on CT Scans: A Comparison of Deep Learning Models
论文作者
论文摘要
新型冠状病毒病（COVID-19）在全球范围内构成了公共卫生紧急情况。这是一种致命疾病，在全球已感染了超过2.3亿人。因此，有必要早期和无法衡量的Covid-19检测。该病毒的证据最常通过RT-PCR检验进行测试。该测试并不是100％可靠的，因为众所周知会产生误报和假否定性。 X射线图像或CT扫描等其他方法显示了肺部的详细成像，并且已被证明更可靠。本文比较了用于通过CT扫描数据集的转移学习技术检测COVID-19的不同深度学习模型。 VGG-16的表现优于所有其他模型，在数据集上的精度为85.33％。
The novel coronavirus disease (COVID-19) constitutes a public health emergency globally. It is a deadly disease which has infected more than 230 million people worldwide. Therefore, early and unswerving detection of COVID-19 is necessary. Evidence of this virus is most commonly being tested by RT-PCR test. This test is not 100% reliable as it is known to give false positives and false negatives. Other methods like X-Ray images or CT scans show the detailed imaging of lungs and have been proven more reliable. This paper compares different deep learning models used to detect COVID-19 through transfer learning technique on CT scan dataset. VGG-16 outperforms all the other models achieving an accuracy of 85.33% on the dataset.
