# Classification of Skin Lesions Using Weighted Majority Voting Ensemble Deep Learning
## 4 Experimental Results
### Stage 2 Evaluation  
The 18 selected learning algorithms from stage 1 were further evaluated across the entire datasets for training and validation in stage 2. Each algorithm was trained to classify skin lesion images into the known classes of AKIEC, BCC, BKL, DF, MEL, NV, SK, SCC, and VASC. The ResNeXt-101 and DPN-1s07 base learners could not be fitted to the training datasets because of the limited GPU capacity of 16 GB compared to other high-end computer vision hardware and were excluded from the stage 2 exercise. The results of a stage 2 evaluation on the entire skin lesion training and validation datasets are highlighted in Table 6. The IG-ResNeXt-101 and SWSL-ResNeXt-101 were the topmost performing models, while the three least performing models were DenseNet-121, DenseNet-161, and VGG-13 when compared to the rest of the learning models. Most of the base learners as specified in Table 6 performed above the average threshold of 0.50 in predicting AKIEC lesions relative to their DSC except for DenseNet-121 and DenseNet-161. The most precise base learner for AKIEC was SWSL-ResNeXt-101 with 82.55% precision which is closely followed by IG-ResNeXt-101 with 82.12% precision. Both IG-ResNeXt-101 and SWSL-ResNeXt-101 were the top two performing base learners whereas DenseNet-121 and DenseNet-161 accounted for the least two performing learners for malignant lesions of BCC, SCC, and MEL. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Learning Algorithm** & **Segmented** & _Sn\%_ & _Sp\%_ & _Acc\%_ & _Ji_ & _DSC_ \\ \hline DenseNet-121 [59] & ✓ & 80.00 & 99.38 & 89.69 & 0.80 & 0.89 \\ DenseNet-121 [59] & x & 88.75 & 100.00 & 94.38 & 0.89 & 0.94 \\ DenseNet-161 [59] & ✓ & 85.00 & 99.38 & 92.19 & 0.84 & 0.92 \\ DenseNet-161 [59] & x & 91.88 & 100.00 & 95.94 & 0.92 & 0.96 \\ DenseNet-169 [59] & ✓ & 70.63 & 99.38 & 85.00 & 0.70 & 0.82 \\ DenseNet-169 [59] & x & 88.75 & 100.00 & 94.38 & 00.89 & 0.94 \\ DenseNet-201 [59] & ✓ & 79.38 & 100.00 & 89.69 & 0.079 & 0.89 \\ DenseNet-201 [59] & x & 90.63 & 99.38 & 95.00 & 0.90 & 0.95 \\ DPN-68 [60] & ✓ & 69.38 & 100.00 & 84.69 & 0.69 & 0.82 \\ DPN-68 [60] & x & 77.50 & 99.38 & 88.44 & 0.77 & 0.87 \\ DPN-92 [60] & ✓ & 66.88 & 99.38 & 83.13 & 0.66 & 0.80 \\ DPN-92 [60] & x & 70.72 & 99.38 & 84.16 & 0.70 & 0.83 \\ DPN-98 [60] & ✓ & 76.25 & 98.75 & 87.50 & 0.75 & 0.86 \\ DPN-98 [60] & x & 91.25 & 100.00 & 95.63 & 0.91 & 0.95 \\ DPN-107 [60] & ✓ & 86.88 & 99.38 & 93.13 & 0.86 & 0.93 \\ DPN-107 [60] & x & 84.38 & 99.38 & 91.88 & 0.84 & 0.91 \\ DPN-131 [60] & ✓ & 72.50 & 99.38 & 85.94 & 0.72 & 0.84 \\ DPN-131 [60] & x & 88.13 & 100.00 & 94.06 & 0.88 & 0.94 \\ ECA-ResNet-101 [61] & ✓ & 48.75 & 100.00 & 74.38 & 0.49 & 0.66 \\ ECA-ResNet-101 [61] & x & 86.88 & 100.00 & 93.44 & 0.87 & 0.93 \\ IG-ResNeXt-101 [61] & ✓ & 76.25 & 98.13 & 87.19 & 0.75 & 0.86 \\ IG-ResNeXt-101 [61] & x & 89.38 & 98.75 & 94.06 & 0.88 & 0.94 \\ SWSL-ResNeXt-101 [63] & ✓ & 80.63 & 98.75 & 89.69 & 0.80 & 0.89 \\ SWSL-ResNeXt-101 [63] & x & 85.00 & 100.00 & 92.50 & 0.85 & 0.92 \\ Inception-ResNet-v2 [64] & ✓ & 75.63 & 95.63 & 85.63 & 0.72 & 0.84 \\ Inception-ResNet-v2 [64] & x & 85.63 & 99.38 & 92.50 & 0.85 & 0.92 \\ ReXNet\_15 [65] & ✓ & 80.63 & 98.75 & 89.69 & 0.80 & 0.89 \\ ReXNet\_15 [65] & x & 88.75 & 100.00 & 94.38 & 0.89 & 0.94 \\ ReXNet\_20 [65] & ✓ & 51.25 & 98.75 & 75.00 & 0.51 & 0.67 \\ ReXNet\_20 [65] & x & 53.13 & 99.38 & 76.25 & 0.53 & 0.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._  
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Most of the base learners recorded low results for SK. ResNet-152 recorded the best DSC of 0.54 followed by DenseNet-169 with 0.53 DSC. This may be a result of its similar characteristics to melanoma that could easily confuse the model training [85]. NV lesion was one of the most correctly classified skin lesions by the base learners with Densenet-161 having the lowest DSC value of 0.84.