# A Soft-Voting Ensemble Classifier for Detecting Patients Affected by COVID-19
## 3 Results
### Comparison with Other Works  
The results have also been compared with those published in two related works found in the literature. The comparison highlighted that both the proposed individual and ensemble classifier models showed better performance. In particular, our results were first compared with those achieved by the classifier model proposed in the work that released the original CC-CCII dataset [14]. In this work, a two-stage classifier model was proposed and experimented. The first stage was aimed at segmenting CT slices using DeepLabv3 [30]. Then, the segmentation results of the slices in each CT scan were stacked to form a volume. The second stage analyzes these volumes to perform a classification using a 3D ResNet [31] network. For three-way classification, this model achieved an accuracy of 92.49% and an AUC of 98.13%. As for NCP-vs-rest, the system achieved an accuracy of 92.49%, a sensitivity of 94.93%, a specificity of 91.13%, and an AUC of 97.97%. The performances of the proposed models have also been compared with the models tested in the work that released the CLEAN-CC-CCII dataset [26]. In this work, the authors benchmarked and compared the performance of a series of state-of-the-art 2D and 3D CNNs according to different depth sizes. In the former case, DenseNet2d, ResNet2d, and ResNeXt2d were used for the experiments, whereas in the latter case, R2 Plus1D, MC3, DenseNet3D, PreAct ResNet3D, ResNeXt3D, and ResNet3D were used for the experiments. The best performance was obtained with DenseNet3D121, which achieved an accuracy of 88.63%, an F1-score of 88.14%, and an AUC of 94.00%. In addition, ResNet3D34 allowed obtaining good results, with an accuracy of 87.83%, an F1-score of 86.04%, and an AUC of 95.90%. Table 9 reports the best performance of the proposed approach with the best performance achieved with the aforementioned works. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model** & **Depth** & **Label** & **ACC** & **P** & **SEN** & **SPE** & **F1** & **AUC** & **MCC** \\ \hline \multirow{4}{*}{Inception-V1} & \multirow{2}{*}{25} & Normal & 97.08\% & 96.24\% & 95.34\% & 98.82\% & 95.79\% & 0.9893 & 0.9447 \\  & & NCP & 98.19\% & 98.79\% & 97.10\% & 99.28\% & 97.94\% & 0.9919 & 0.9672 \\  & & CP & 97.93\% & 96.12\% & 98.31\% & 97.55\% & 97.20\% & 0.9933 & 0.9546 \\ \cline{2-10}  & \multirow{3}{*}{35} & Normal & 97.47\% & 95.78\% & 96.27\% & 98.66\% & 96.02\% & 0.9970 & 0.9476 \\  & & NCP & 98.21\% & 98.73\% & 97.17\% & 99.24\% & 97.94\% & 0.9972 & 0.9672 \\  & & CP & 97.78\% & 96.53\% & 97.72\% & 97.83\% & 97.12\% & 0.9961 & 0.9533 \\ \hline \multirow{4}{*}{Inception-V3} & \multirow{2}{*}{25} & Normal & 95.68\% & 93.00\% & 93.58\% & 97.78\% & 93.29\% & 0.9942 & 0.9116 \\  & & NCP & 96.85\% & 98.23\% & 94.73\% & 98.96\% & 96.45\% & 0.9956 & 0.9439 \\ \cline{1-1}  & & CP & 96.35\% & 93.76\% & 96.68\% & 96.02\% & 95.20\% & 0.9942 & 0.9218 \\ \cline{1-1} \cline{2-10}  & \multirow{3}{*}{35} & Normal & 96.07\% & 94.09\% & 94.00\% & 98.14\% & 94.04\% & 0.9921 & 0.9216 \\ \cline{1-1}  & & NCP & 97.08\% & 98.37\% & 95.13\% & 99.04\% & 96.72\% & 0.9961 & 0.9482 \\ \cline{1-1}  & & CP & 96.66\% & 94.02\% & 97.14\% & 96.18\% & 95.55\% & 0.9931 & 0.9276 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Soft-voting ensemble models—Class-wise performances for Inception-V1 and Inception-V3 using 25 and 35 slices in terms of balanced accuracy, precision, sensitivity, specificity, F1-score, Area Under the ROC Curve, and Matthews Correlation Coefficient. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **ACC** & **P** & **SEN** & **SPE** & **F1** & **AUC** \\ \hline proposed & 98.21\% & 98.73\% & 97.17\% & 99.24\% & 97.94\% & 0.9972 \\ [PERSON] et al. [14] & 92.49\% & 92.20\% & 94.93\% & 91.13\% & 93.55\% & 0.9797 \\ [PERSON] et al. [26] & 88.63\% & 90.28\% & 86.09\% & 94.35\% & 88.14\% & 0.9400 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance comparison.