## 4 Discussion
### Performance Analysis
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Models** & **Class** & **Precision (\%)** & \begin{tabular}{l} **Sensitivity** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{l} **Specificity** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{l} **F1-Score (\%)** \\ \end{tabular} & **Accuracy (\%)** \\ \hline \multirow{4}{*}{VGG16} & COVID +ve & 0.970 & 0.978 & 0.985 & 0.974 & \multirow{4}{*}{96.68} \\ \cline{2-5}  & COVID -ve & 0.963 & 0.957 & 0.982 & 0.960 & \\ \cline{2-5}  & Pneumonia & 0.967 & 0.965 & 0.984 & 0.966 & \\ \cline{2-5}  & COVID +ve & 0.970 & 0.981 & 0.985 & 0.975 & \multirow{4}{*}{96.75} \\ \cline{2-5}  & COVID -ve & 0.963 & 0.957 & 0.982 & 0.960 & \\ \cline{2-5}  & Pneumonia & 0.970 & 0.965 & 0.985 & 0.967 & \\ \cline{2-5}  & COVID +ve & 0.972 & 0.974 & 0.986 & 0.973 & \multirow{4}{*}{96.90} \\ \cline{2-5}  & COVID -ve & 0.969 & 0.959 & 0.985 & 0.964 & \\ \cline{2-5}  & Pneumonia & 0.966 & 0.974 & 0.983 & 0.970 & \\ \hline \multirow{4}{*}{VGG16 + Sugeno Integral} & COVID +ve & 0.970 & 0.978 & 0.985 & 0.974 & \multirow{4}{*}{97.04} \\ \cline{2-5}  & COVID -ve & 0.971 & 0.957 & 0.986 & 0.964 & \\ \cline{2-5}  & Pneumonia & 0.970 & 0.976 & 0.985 & 0.973 & \\ \hline \multirow{4}{*}{GoogleNet} & COVID +ve & 0.978 & 0.981 & 0.989 & 0.979 & \multirow{4}{*}{97.91} \\ \cline{2-5}  & COVID -ve & 0.978 & 0.974 & 0.989 & 0.976 & \\ \cline{2-5}  & Pneumonia & 0.981 & 0.983 & 0.990 & 0.982 & \\ \hline \multirow{4}{*}{GoogleNet + Majority Voting} & COVID +ve & 0.978 & 0.985 & 0.989 & 0.982 & \multirow{4}{*}{98.05} \\ \cline{2-5}  & COVID -ve & 0.980 & 0.976 & 0.990 & 0.978 & \\ \cline{2-5}  & Pneumonia & 0.983 & 0.981 & 0.991 & 0.982 & \\ \hline \multirow{4}{*}{GoogleNet + Sugeno Integral} & COVID +ve & 0.981 & 0.983 & 0.990 & 0.982 & \multirow{4}{*}{98.27} \\ \cline{2-5}  & COVID -ve & 0.987 & 0.978 & 0.994 & 0.983 & \\ \cline{2-5}  & Pneumonia & 0.981 & 0.987 & 0.990 & 0.984 & \\ \hline \multirow{4}{*}{GoogleNet + Choquet Fuzzy} & COVID +ve & 0.983 & 0.987 & 0.991 & 0.985 & \multirow{4}{*}{98.48} \\ \cline{2-5}  & COVID -ve & 0.989 & 0.978 & 0.995 & 0.984 & \\ \cline{2-5}  & Pneumonia & 0.983 & 0.989 & 0.991 & 0.986 & \\ \hline \multirow{4}{*}{Xception} & COVID +ve & 0.983 & 0.983 & 0.991 & 0.983 & \multirow{4}{*}{98.20} \\ \cline{2-5}  & COVID -ve & 0.987 & 0.978 & 0.994 & 0.983 & \\ \cline{2-5}  & Pneumonia & 0.976 & 0.985 & 0.988 & 0.981 & \\ \hline \multirow{4}{*}{Xception + Majority Voting} & COVID +ve & 0.985 & 0.985 & 0.992 & 0.985 & \multirow{4}{*}{98.56} \\ \cline{2-5}  & COVID -ve & 0.985 & 0.985 & 0.992 & 0.985 & \\ \cline{2-5}  & Pneumonia & 0.987 & 0.987 & 0.994 & 0.987 & \\ \hline \multirow{4}{*}{Xception + Sugeno Integral} & COVID +ve & 0.991 & 0.996 & 0.996 & 0.994 & \multirow{4}{*}{99.28} \\ \cline{2-5}  & COVID -ve & 0.993 & 0.989 & 0.997 & 0.991 & \\ \cline{2-5}  & Pneumonia & 0.994 & 0.994 & 0.997 & 0.994 & \\ \hline \multirow{4}{*}{Proposed Model} & COVID +ve & 0.996 & 0.998 & 0.998 & 0.997 & \multirow{4}{*}{99.57} \\ \cline{2-5}  & COVID -ve & 0.998 & 0.994 & 0.999 & 0.996 & \\ \cline{2-5}  & Pneumonia & 0.994 & 0.996 & 0.997 & 0.995 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance metrics for various transfer learning models associated with fuzzy ensemble techniques.