  Coronavirus has caused hundreds of thousands of deaths. Fatalities could
decrease if every patient could get suitable treatment by the healthcare
system. Machine learning, especially computer vision methods based on deep
learning, can help healthcare professionals diagnose and treat COVID-19
infected cases more efficiently. Hence, infected patients can get better
service from the healthcare system and decrease the number of deaths caused by
the coronavirus. This research proposes a method for segmenting infected lung
regions in a CT image. For this purpose, a convolutional neural network with an
attention mechanism is used to detect infected areas with complex patterns.
Attention blocks improve the segmentation accuracy by focusing on informative
parts of the image. Furthermore, a generative adversarial network generates
synthetic images for data augmentation and expansion of small available
datasets. Experimental results show the superiority of the proposed method
compared to some existing procedures.
