Table 5.

A comparison of Our model with previous approaches for COVID-19 Classification.

• (c) In case the null hypothesis is rejected, the performance of our model is statistically different from the other model. We consider it to be a win for our model if the mean accuracy value of our model is greater than that of the competing model. We denote it be a •. Otherwise, it's a loss for our model. We denote it by a ◦.
• (d)
  If t-test does not reject the null hypothesis, the performance of our model is not statistically different from the other model and we consider it to be a tie. A tie will be represented by no symbol.

Approach	Classes	COVID-19 Images	Balanced Dataset?	5-folds CV?	Statistical Comparison?	Tuning Hyper parameters?	Accuracy
Our Model	4	1000					98.45%
Covid-Net [35]	3	180	×	×	×	×	93.3%
CoroNet [64]	4	284	×	×	×	×	89.6%
XGB [86]	4	130	×		×	×	79.52%
DELT [87]	4	305	×		×	×	90.13%
COVID-ResNet [74]	4	68	×	×	×	×	96.23%