In this paper, we present a hybrid deep learning framework named CTNet which combines convolutional neural network and transformer together for the detection of COVID-19 via 3D chest CT images. It consists of a CNN feature extractor module with SE attention to extract sufficient features from CT scans, together with a transformer model to model the discriminative features of the 3D CT scans. Compared to previous works, CTNet provides an effective and efficient method to perform COVID-19 diagnosis via 3D CT scans with data resampling strategy. Advanced results on a large and public benchmarks, COV19-CT-DB database was achieved by the proposed CTNet, over the state-of-the-art baseline approachproposed together with the dataset.

翻译：在本文中,我们提出了一个称为CTNet的混合深层次学习框架,将进化神经网络和变压器结合起来,通过3D胸部CT图像探测COVID-19,其中包括CNN特效提取器模块,SE注意从CT扫描中提取足够的特征,以及一个变压器模型,以模拟3DCT扫描的歧视性特征。与以前的工作相比,CTNet提供了一种有效和高效的方法,通过3DCT扫描和数据取样战略进行COVID-19诊断。在大型和公共基准方面,拟议的CTNet实现了COV19-CT-DB数据库的高级结果,超过了与数据集一起提出的最先进的基线方法。