  The new coronavirus infection has shocked the world since early 2020 with its
aggressive outbreak. Rapid detection of the disease saves lives, and relying on
medical imaging (Computed Tomography and X-ray) to detect infected lungs has
shown to be effective. Deep learning and convolutional neural networks have
been used for image analysis in this context. However, accurate identification
of infected regions has proven challenging for two main reasons. Firstly, the
characteristics of infected areas differ in different images. Secondly,
insufficient training data makes it challenging to train various machine
learning algorithms, including deep-learning models. This paper proposes an
approach to segment lung regions infected by COVID-19 to help cardiologists
diagnose the disease more accurately, faster, and more manageable. We propose a
bifurcated 2-D model for two types of segmentation. This model uses a shared
encoder and a bifurcated connection to two separate decoders. One decoder is
for segmentation of the healthy region of the lungs, while the other is for the
segmentation of the infected regions. Experiments on publically available
images show that the bifurcated structure segments infected regions of the
lungs better than state of the art.
