  The novelty of the COVID-19 disease and the speed of spread has created a
colossal chaos, impulse among researchers worldwide to exploit all the
resources and capabilities to understand and analyze characteristics of the
coronavirus in term of the ways it spreads and virus incubation time. For that,
the existing medical features like CT and X-ray images are used. For example,
CT-scan images can be used for the detection of lung infection. But the
challenges of these features such as the quality of the image and infection
characteristics limitate the effectiveness of these features. Using artificial
intelligence (AI) tools and computer vision algorithms, the accuracy of
detection can be more accurate and can help to overcome these issues. This
paper proposes a multi-task deep-learning-based method for lung infection
segmentation using CT-scan images. Our proposed method starts by segmenting the
lung regions that can be infected. Then, segmenting the infections in these
regions. Also, to perform a multi-class segmentation the proposed model is
trained using the two-stream inputs. The multi-task learning used in this paper
allows us to overcome shortage of labeled data. Also, the multi-input stream
allows the model to do the learning on many features that can improve the
results. To evaluate the proposed method, many features have been used. Also,
from the experiments, the proposed method can segment lung infections with a
high degree performance even with shortage of data and labeled images. In
addition, comparing with the state-of-the-art method our method achieves good
performance results.
