# Enhancing Skin Lesion Classification Performance with the ABC Ensemble Model
## 4 Results
### Overall Classification Performance  
The following is an explanation of the performance evaluation results from the skin lesion classification experiment using the HAM10000 dataset. The overall performance for skin lesion classification on the HAM10000 dataset is shown in Table 3. The 'Model' column represents the 15 experimental models, and the 'Preprocessing Method' column indicates the five preprocessing methods: ROI for ROI extraction, Ups. for Upsampling, Seg. for Segmentation, Adj. for Adjust Hue, and Mor. for Morphological Transformation. The best-performing model is highlighted in bold, and the second-best model is underlined. The results for all 15 experimental models are presented as the average accuracy, recall, precision, and F1-score over five experiments, each conducted with 5-fold cross-validation. The model with the best overall performance is the Ensemble (w/ weights, w/ ABCD, Ours), which consistently outperformed all other models across all metrics. Specifically, it achieved an accuracy of 0.9326, a recall of 0.9316, a precision of 0.9310, and an F1-score of 0.9302. This model demonstrates that the combination of weighted soft voting and preprocessing with the ABCD rule leads to superior classification performance. The second-best model is the Ensemble (w/ weights, w/o ABCD), which also used weighted soft voting but did not incorporate the ABCD rule. This model delivered results, with an accuracy of 0.9232, a recall of 0.9223, a precision of 0.9226, and an F1-score of 0.9196. The best model (our ABC ensemble model)'s F1-score of 0.9302 is higher than the second-best model's F1-score of 0.9196, with a difference of approximately 1.06%, showing the significant impact of incorporating the ABCD rule in the classification process. This difference highlights how ABCD-based preprocessing allows for the better classification of lesion characteristics. Among the seven models without preprocessing (1 st model-7 th model in Table 3), EfficientNet-B0 showed the best performance, achieving an accuracy of 0.9025 and an F1-score of 0.8997. AlexNet showed the lowest performance, with an accuracy of 0.8458 and an F1-score of 0.8412. As a result, when building the ensemble block, we excluded the lower-performing GoogLeNet and AlexNet models and instead used ViT-B/16, DenseNet121, EfficientNet-B0, MobileNetV2, and ResNet50 to construct the block. Despite its competitive performance, the lack of preprocessing limited its ability to capture finer details and \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Preprocessing Method**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**F1-Score**} \\ \cline{2-2} \cline{5-8}  & **ROI.** & & & & & & & & \\ \hline GoogLeNet & - & - & - & - & - & 0.8816 & 0.8779 & 0.8816 & 0.8762 \\ AlexNet & - & - & - & - & - & 0.8458 & 0.8424 & 0.8458 & 0.8412 \\ ViT-B/16 & - & - & - & - & - & 0.8937 & 0.8905 & 0.8932 & 0.8900 \\ DenseNet121 & - & - & - & - & - & 0.9023 & 0.9001 & 0.9022 & 0.8993 \\ EfficientNet-B0 & - & - & - & - & - & 0.9025 & 0.9000 & 0.9024 & 0.8997 \\ MobileNetV2 & - & - & - & - & - & 0.8909 & 0.8878 & 0.8909 & 0.8880 \\ ResNet50 & - & - & - & - & - & 0.8883 & 0.8854 & 0.8883 & 0.8852 \\ \hline ViT-B/16 & ✓ & - & - & - & - & 0.9090 & 0.9059 & 0.9073 & 0.9053 \\ DenseNet121 & ✓ & ✓ & - & - & - & 0.8913 & 0.8900 & 0.8913 & 0.8893 \\ EfficientNet-B0 & ✓ & - & ✓ & - & - & 0.9073 & 0.9057 & 0.9073 & 0.9051 \\ MobileNetV2 & ✓ & - & - & ✓ & - & 0.8999 & 0.8987 & 0.8999 & 0.8982 \\ ResNet50 & ✓ & - & - & - & ✓ & 0.8999 & 0.8982 & 0.8999 & 0.8973 \\ \hline Ensemble (w/o ABCD) & - & - & - & - & - & 0.9154 & 0.9137 & 0.9154 & 0.9129 \\ Ensemble (w/ weights, w/o ABCD) & - & - & - & - & - & 0.9232 & 0.9223 & 0.9226 & 0.9196 \\
**Ensemble (w/ weights, w/ ABCD, Ours)** & ✓ & ✓ & ✓ & ✓ & **0.9326** & **0.9316** & **0.9310** & **0.9302** \\ \hline \hline \end{tabular}
\end{table}