  In the last three years, the world has been facing a global crisis caused by
Covid-19 pandemic. Medical imaging has been playing a crucial role in the
fighting against this disease and saving the human lives. Indeed, CT-scans has
proved their efficiency in diagnosing, detecting, and following-up the Covid-19
infection. In this paper, we propose a new Transformer-CNN based approach for
Covid-19 infection segmentation from the CT slices. The proposed D-TrAttUnet
architecture has an Encoder-Decoder structure, where compound Transformer-CNN
encoder and Dual-Decoders are proposed. The Transformer-CNN encoder is built
using Transformer layers, UpResBlocks, ResBlocks and max-pooling layers. The
Dual-Decoder consists of two identical CNN decoders with attention gates. The
two decoders are used to segment the infection and the lung regions
simultaneously and the losses of the two tasks are joined. The proposed
D-TrAttUnet architecture is evaluated for both Binary and Multi-classes
Covid-19 infection segmentation. The experimental results prove the efficiency
of the proposed approach to deal with the complexity of Covid-19 segmentation
task from limited data. Furthermore, D-TrAttUnet architecture outperforms three
baseline CNN segmentation architectures (Unet, AttUnet and Unet++) and three
state-of-the-art architectures (AnamNet, SCOATNet and CopleNet), in both Binary
and Mutli-classes segmentation tasks.
