  Rapid and precise diagnosis of COVID-19 is one of the major challenges faced
by the global community to control the spread of this overgrowing pandemic. In
this paper, a hybrid neural network is proposed, named CovTANet, to provide an
end-to-end clinical diagnostic tool for early diagnosis, lesion segmentation,
and severity prediction of COVID-19 utilizing chest computer tomography (CT)
scans. A multi-phase optimization strategy is introduced for solving the
challenges of complicated diagnosis at a very early stage of infection, where
an efficient lesion segmentation network is optimized initially which is later
integrated into a joint optimization framework for the diagnosis and severity
prediction tasks providing feature enhancement of the infected regions.
Moreover, for overcoming the challenges with diffused, blurred, and varying
shaped edges of COVID lesions with novel and diverse characteristics, a novel
segmentation network is introduced, namely Tri-level Attention-based
Segmentation Network (TA-SegNet). This network has significantly reduced
semantic gaps in subsequent encoding decoding stages, with immense
parallelization of multi-scale features for faster convergence providing
considerable performance improvement over traditional networks. Furthermore, a
novel tri-level attention mechanism has been introduced, which is repeatedly
utilized over the network, combining channel, spatial, and pixel attention
schemes for faster and efficient generalization of contextual information
embedded in the feature map through feature re-calibration and enhancement
operations. Outstanding performances have been achieved in all three-tasks
through extensive experimentation on a large publicly available dataset
containing 1110 chest CT-volumes that signifies the effectiveness of the
proposed scheme at the current stage of the pandemic.
