  The pandemic of novel SARS-CoV-2 also known as COVID-19 has been spreading
worldwide, causing rampant loss of lives. Medical imaging such as CT, X-ray,
etc., plays a significant role in diagnosing the patients by presenting the
visual representation of the functioning of the organs. However, for any
radiologist analyzing such scans is a tedious and time-consuming task. The
emerging deep learning technologies have displayed its strength in analyzing
such scans to aid in the faster diagnosis of the diseases and viruses such as
COVID-19. In the present article, an automated deep learning based model,
COVID-19 hierarchical segmentation network (CHS-Net) is proposed that functions
as a semantic hierarchical segmenter to identify the COVID-19 infected regions
from lungs contour via CT medical imaging using two cascaded residual attention
inception U-Net (RAIU-Net) models. RAIU-Net comprises of a residual inception
U-Net model with spectral spatial and depth attention network (SSD) that is
developed with the contraction and expansion phases of depthwise separable
convolutions and hybrid pooling (max and spectral pooling) to efficiently
encode and decode the semantic and varying resolution information. The CHS-Net
is trained with the segmentation loss function that is the defined as the
average of binary cross entropy loss and dice loss to penalize false negative
and false positive predictions. The approach is compared with the recently
proposed approaches and evaluated using the standard metrics like accuracy,
precision, specificity, recall, dice coefficient and Jaccard similarity along
with the visualized interpretation of the model prediction with GradCam++ and
uncertainty maps. With extensive trials, it is observed that the proposed
approach outperformed the recently proposed approaches and effectively segments
the COVID-19 infected regions in the lungs.
