  The devastation caused by the coronavirus pandemic makes it imperative to
design automated techniques for a fast and accurate detection. We propose a
novel non-invasive tool, using deep learning and imaging, for delineating
COVID-19 infection in lungs. The Ensembling Attention-based Multi-scaled
Convolution network (EAMC), employing Leave-One-Patient-Out (LOPO) training,
exhibits high sensitivity and precision in outlining infected regions along
with assessment of severity. The Attention module combines contextual with
local information, at multiple scales, for accurate segmentation. Ensemble
learning integrates heterogeneity of decision through different base
classifiers. The superiority of EAMC, even with severe class imbalance, is
established through comparison with existing state-of-the-art learning models
over four publicly-available COVID-19 datasets. The results are suggestive of
the relevance of deep learning in providing assistive intelligence to medical
practitioners, when they are overburdened with patients as in pandemics. Its
clinical significance lies in its unprecedented scope in providing low-cost
decision-making for patients lacking specialized healthcare at remote
locations.
