  The issue of COVID-19, increasing with a massive mortality rate. This led to
the WHO declaring it as a pandemic. In this situation, it is crucial to perform
efficient and fast diagnosis. The reverse transcript polymerase chain reaction
(RTPCR) test is conducted to detect the presence of SARS-CoV-2. This test is
time-consuming and instead chest CT (or Chest X-ray) can be used for a fast and
accurate diagnosis. Automated diagnosis is considered to be important as it
reduces human effort and provides accurate and low-cost tests. The
contributions of our research are three-fold. First, it is aimed to analyse the
behaviour and performance of variant vision models ranging from Inception to
NAS networks with the appropriate fine-tuning procedure. Second, the behaviour
of these models is visually analysed by plotting CAMs for individual networks
and determining classification performance with AUCROC curves. Thirdly, stacked
ensembles techniques are imparted to provide higher generalisation on combining
the fine-tuned models, in which six ensemble neural networks are designed by
combining the existing fine-tuned networks. Implying these stacked ensembles
provides a great generalization to the models. The ensemble model designed by
combining all the fine-tuned networks obtained a state-of-the-art accuracy
score of 99.17%. The precision and recall for the COVID-19 class are 99.99% and
89.79% respectively, which resembles the robustness of the stacked ensembles.
