# Classification of Skin Lesions Using Weighted Majority Voting Ensemble Deep Learning
## 4 Experimental Results
### Stage 2 Evaluation
INDB was correctly classified by averaging 0.95 for most of the base learners besides the ReXNet_1.5 which recorded a value of 0.84 for the SC. ECA-ResNet-101 recorded the most sensitivity of 96.00% for VASC. ResNet-152 had the best discriminating capability for VASC having a 0.95 DSC. However, both DenseNet-121 and DenseNet-161 performed poorly when compared to other base learners in the prediction of VASC lesions. The IG-ResNeXt-101 and SWSL-ResNeXt-101 were again the top two performing base learners in the classification of BKL, while DenseNet-161 recorded the worst performance of 0.37 DSC. The validation of the base learners against DF lesions indicated that DenseNet-201 provided the most discriminating power of 0.89 for DSC and DenseNet-121 had the least discriminating power of 0.18 for DSC. The metrics that provided the most guided evaluation for the base learners were DSC, Ji, and multiclass accuracy (MAcc). It can therefore be argued that the solitary application of simple balance accuracy, sensitivity, specificity, and precision without the compliment of DSC, Ji, and MAcc might not be a good representation of the discriminating capability of a given learning algorithm. Both SMVE and WMVE algorithms were observed to outperform each base learner for most of the skin lesion classes because they recorded the best multiclass accuracies of 92.33% and 92.84%, respectively. The two algorithms shared the best specificity result of 99.83% with the Inception-ResNet-v2 base learner for the prediction of AKIEC. However, the WMVE algorithm provided the best result for sensitivity (73.30%), balance accuracy \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Learning Algorithm** & **Class** & **Sn\%** & **Sp\%** & **Acc\%** & **MAcc\%** & **Pr\%** & **Ji** & **DSC** & **MCC** & **Weight** \\ \hline \multirow{8}{*}{ReXNet\_1.5 [65]} & AKIEC & 64.77 & 99.51 & 98.99 & & 67.06 & 0.49 & 0.66 & 0.65 & \\  & BCC & 79.66 & 99.40 & 98.32 & & 88.52 & 0.72 & 0.84 & 0.83 & \\  & BKL & 71.46 & 98.76 & 97.62 & & 71.46 & 0.56 & 0.71 & 0.70 & \\  & DF & 68.09 & 99.97 & 99.84 & & 88.89 & 0.63 & 0.77 & 0.78 & \\  & MEL & 63.60 & 98.12 & 95.16 & 89.20 & & 75.99 & 0.53 & 0.69 & 0.67 & \\  & NV & 91.37 & 94.23 & 93.35 & & 87.57 & 0.81 & 0.89 & 0.85 & \\  & SCC & 68.85 & 99.81 & 99.49 & & 79.25 & 0.58 & 0.74 & 0.74 & \\  & SK & 34.12 & 99.89 & 99.41 & & 60.05 & 0.30 & 0.46 & 0.48 & \\  & VASC & 88.00 & 99.95 & 99.90 & & 88.00 & 0.79 & 0.88 & 0.88 & \\  & INDB & 97.42 & 95.35 & 96.32 & & 94.88 & 0.93 & 0.96 & 0.93 & \\ \hline \multirow{8}{*}{SMVE (Ours)} & AKIEC & 70.45 & 99.83 & 99.38 & & 86.11 & 0.63 & 0.78 & 0.78 & \\  & BCC & 90.61 & 99.56 & 99.07 & & 92.34 & 0.84 & 0.91 & 0.91 & \\  & BKL & 80.70 & 99.20 & 98.43 & & 81.54 & 0.68 & 0.81 & 0.80 & \\  & DF & 89.36 & 99.96 & 99.91 & & 89.36 & 0.81 & 0.89 & 0.89 & \\  & MEL & 74.10 & 98.63 & 96.53 & & 83.54 & 0.65 & 0.79 & 0.77 & \\  & NV & 93.16 & 96.47 & 95.45 & & 92.16 & 0.86 & 0.93 & 0.89 & \\  & SCC & 81.15 & 99.89 & 99.69 & & 88.39 & 0.73 & 0.85 & 0.85 & \\  & SK & 42.35 & 99.79 & 99.37 & & 60.00 & 0.33 & 0.50 & 0.50 & \\  & VASC & 94.00 & 100.00 & 99.97 & & 100.00 & 0.94 & 0.97 & 0.97 & \\  & INDB & 98.10 & 95.73 & 96.84 & & 95.30 & 0.94 & 0.97 & 0.94 \\ \hline \multirow{8}{*}{WMVE (Ours)} & AKIEC & 73.30 & 99.83 & 99.43 & & 87.16 & 0.66 & 0.80 & 0.80 & \\  & BCC & 91.24 & 99.57 & 99.12 & & 92.54 & 0.85 & 0.92 & 0.91 \\  & BKL & 82.75 & 99.29 & 98.60 & & 83.61 & 0.71 & 0.83 & 0.82 \\  & DF & 93.62 & 99.96 & 99.93 & & 89.80 & 0.85 & 0.92 & 0.92 \\  & MEL & 76.90 & 98.63 & 96.77 & & 84.04 & 0.67 & 0.80 & 0.79 \\  & NV & 93.18 & 96.88 & 95.74 & & 93.00 & 0.87 & 0.93 & 0.90 \\  & SCC & 82.79 & 99.88 & 99.70 & & 87.83 & 0.74 & 0.85 & 0.85 \\  & SK & 41.18 & 99.78 & 99.35 & & 57.38 & 0.32 & 0.48 & 0.48 \\  & VASC & 92.00 & 99.98 & 99.95 & & 95.83 & 0.88 & 0.94 & 0.94 \\  & INDB & 98.26 & 96.03 & 97.08 & & 95.63 & 0.94 & 0.97 & 0.94 \\ \hline \hline \end{tabular}
\end{table}