Deep learning approaches have attracted a lot of attention in the automatic detection of Covid-19 and transfer learning is the most common approach. However, majority of the pre-trained models are trained on color images, which can cause inefficiencies when fine-tuning the models on Covid-19 images which are often grayscale. To address this issue, we propose a deep learning architecture called CovidNet which requires a relatively smaller number of parameters. CovidNet accepts grayscale images as inputs and is suitable for training with limited training dataset. Experimental results show that CovidNet outperforms other state-of-the-art deep learning models for Covid-19 detection.

深度学习方法在自动检测Covid-19中引起了很多关注，转移学习是最常见的方法。但是，大多数预先训练的模型都是在彩色图像上训练的，当在通常为灰度的Covid-19图像上微调模型时，可能会导致效率低下。为了解决这个问题，我们提出了一种名为CovidNet的深度学习架构，该架构需要数量相对较少的参数。CovidNet接受灰度图像作为输入，适用于训练数据集有限的训练。实验结果表明，对于Covid-19检测，CovidNet优于其他最新的深度学习模型。