COVID-19 has led to hundreds of millions of cases and millions of deaths
worldwide since its onset. The fight against this pandemic is on-going on
multiple fronts. While vaccinations are picking up speed, there are still
billions of unvaccinated people. In this fight against the virus, diagnosis of
the disease and isolation of the patients to prevent any spread play a huge
role. Machine Learning approaches have assisted in the diagnosis of COVID-19
cases by analyzing chest X-rays and CT-scan images of patients. To push
algorithm development and research in this direction of radiological diagnosis,
a challenge to classify CT-scan series was organized in conjunction with ICCV,
2021. In this research we present a simple and shallow Convolutional Neural
Network based approach, TeliNet, to classify these CT-scan images of COVID-19
patients presented as part of this competition. Our results outperform the F1
`macro' score of the competition benchmark and VGGNet approaches. Our proposed
solution is also more lightweight in comparison to the other methods.

利用卷积神经网络 TeliNet，我们针对竞赛中提供的 CT 扫描图像，对 COVID-19 患者进行分类，结果优于竞赛基准和 VGGNet 方法，并且相对于其他方法，我们提出的解决方案更轻量级。