# Classification of Skin Lesions Using Weighted Majority Voting Ensemble Deep Learning
## 4 Experimental Results
### Stage 1 Evaluation  
Table 5 shows the result of each learning algorithm in classifying malignancy skin lesions over 2000 balanced datasets. The evaluation of the 26 learning algorithms yielded a total of 52 different experimental analyses for both the segmented and non-segmented skin lesion images. A total of 18 learning algorithms selected for further evaluation met the minimum requirement of a 0.92 dice coefficient. The chosen learning algorithms include VGG (13 and 19), ResNet (101 and 152), ResNeXt (50 and 101), ECA-ResNet-101, IG-ResNeXt-101, SWSL-ResNeXt-101, DenseNet (121, 161, 169, and 201), DPN (98, 107, and 131), Inception-ResNet-v2, and ReXNet_1.5. Most of the learning algorithms, interestingly, performed better without prior image segmentation when compared to the image segmentation episode. However, the DPN-107 algorithm achieved a dice coefficient of 0.93 on the segmented skin lesion images and a dice coefficient of 0.91 without prior image segmentation. This result seems to agree with the observation made in [52] that prior lesion segmentation did not significantly improve the classification performance for most of the base learners. \begin{table} \begin{tabular}{c c c c c} \hline \hline
**Learning Algorithm** & **Weight Decay** & **Learning Rate** & **Batch Size** & **Drop Out a ** \\ \hline VGG (11, 16) [56] & 0.0100 & 0.0010 & 12 & 0.2500 to 0.5000 \\ VGG-13 [56] & 0.0100 & 0.0006 & 24 & 0.1250 \\ VGG-19 [56] & 0.0100 & 0.0007 & 24 & 0.1250 \\ ResNet (18, 34, 50) [57] & 0.0100 & 0.0010 & 12 & 0.2500 to 0.5000 \\ ResNet (101 and 152) [57] & 0.0100 & 0.0200 & 24 & 0.2500 to 0.5000 \\ ResNeXt-50 [58] & 0.0010 & 0.0020 & 24 & 0.2500 to 0.5000 \\ ResNeXt-101 [58] & 0.0100 & 0.0010 & 8 & 0.2500 to 0.5000 \\ DenseNet (121, 169, 201) [59] & 0.0100 & 0.0020 & 24 & 0.250 to 0.5000 \\ DenseNet-161) [59] & 0.0100 & 0.0010 & 20 & 0.250 to 0.5000 \\ DPN (68, 92) [60] & 0.0100 & 0.0010 & 12 & 0.250 to 0.5000 \\ DPN-98 [60] & 0.0100 & 0.0004 & 32 & 0.1250 \\ DPN-107 [60] & 0.0100 & 0.0008 & 32 & 0.1250 \\ DPN-131 [60] & 0.0100 & 0.0006 & 32 & 0.1250 \\ ECA-ResNet-101 [61] & 0.0100 & 0.0025 b, 0.0017 c  & 32  \\ IG-ResNeXt-101 [62] & 0.0100 & 0.0044 b, 0.0007 c  & 24 , 32  \\ SWSL-ResNeXt-101 [63] & 0.0100 & 0.0001 b, 0.0006 c  & 32  \\ Inception-ResNet-v2 [64] & 0.0100 & 0.0036 & 32 & 0.1250 \\ ReXNet\_1.5 [65] & 0.0100 & 0.0020 & 24 & 0.1250 \\ ReXNet\_2.0 [65] & 0.0100 & 0.0010 & 32 & 0.1250 \\ \hline \hline \end{tabular} \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Learning Algorithm** & **Segmented** & _Sn\%_ & _Sp\%_ & _Acc\%_ & _Ji_ & **DSC** \\ \hline VGG-11 [56] & ✓ & 70.63 & 99.38 & 90.63 & 0.70 & 0.82 \\ VGG-11 [56] & \(x\) & 75.00 & 98.75 & 90.31 & 0.74 & 0.85 \\ VGG-13 [56] & _�_ & 63.75 & 98.75 & 92.81 & 0.63 & 0.77 \\ VGG-13 [56] & \(x\) & 88.75 & 98.13 & 95.31 & 0.87 & **0.93** \\ VGG-16 [56] & ✓ & 71.25 & 98.13 & 91.25 & 0.70 & 0.82 \\ VGG-16 [56] & \(x\) & 66.25 & 99.38 & 93.44 & 0.66 & 0.79 \\ VGG-19 [56] & ✓ & 78.75 & 98.13 & 94.06 & 0.77 & 0.87 \\ VGG-19 [56] & \(x\) & 89.38 & 100.00 & 98.44 & 0.89 & 0.94 \\ ResNet-18 [57] & ✓ & 44.38 & 99.38 & 93.12 & 0.44 & 0.61 \\ ResNet-18 [57] & \(x\) & 75.63 & 100.00 & 89.06 & 0.76 & 0.86 \\ ResNet-34 [57] & ✓ & 78.75 & 98.75 & 97.50 & 0.78 & 0.88 \\ ResNet-34 [57] & \(x\) & 50.00 & 100.00 & 90.94 & 0.50 & 0.67 \\ ResNet-50 [57] & ✓ & 78.75 & 98.13 & 90.63 & 0.77 & 0.87 \\ ResNet-50 [57] & \(x\) & 65.63 & 100.00 & 91.56 & 0.66 & 0.79 \\ ResNet-101 [57] & ✓ & 50.00 & 99.38 & 94.69 & 0.50 & 0.66 \\ ResNet-101 [57] & ✓ & 90.00 & 97.50 & 93.75 & 0.88 & 0.94 \\ ResNet-152 [57] & ✓ & 68.75 & 99.38 & 91.56 & 0.68 & 0.81 \\ ResNet-152 [57] & \(x\) & 89.38 & 99.38 & 94.38 & 0.89 & 0.94 \\ ResNeXt-50 [58] & ✓ & 35.63 & 100.00 & 85.63 & 0.36 & 0.53 \\ ResNeXt-50 [58] & \(x\) & 89.38 & 99.38 & 94.38 & 0.89 & 0.94 \\ ResNeXt-101 [58] & ✓ & 80.00 & 99.38 & 95.00 & 0.80 & 0.89 \\ ResNeXt-101 [58] & \(x\) & 85.00 & 99.38 & 95.00 & 0.84 & 0.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Binary classification of malignant skin lesions.