# Enhancing Skin Lesion Classification Performance with the ABC Ensemble Model
## 4 Results
### Grad-CAM Analysis  
Our model applies different preprocessing methods across the five blocks of the ensemble model, based on the clinical guideline, the ABCD rule. Block 1 and Block 2 from Figure 4 are designed to capture general image characteristics, Block 3 focuses on the symmetry of the lesion, Block 4 blurs color information to emphasize the lesion's border, and Block 5 concentrates on the color of the lesion. To ensure that each preprocessing method works as intended, we performed Grad-CAM analysis, with the results shown in Figure 15. This figure shows the original images in the first row and their corresponding Grad-CAM visualizations in the second row for the five blocks.  
Grad-CAM visualizations use colors to indicate the model's focus. Warmer colors such as red and yellow highlight areas that receive more attention, while cooler colors like blue indicate less focus.  
In Figure 15a for the Generality Training, the model demonstrates consistent attention across the entire lesion area. This indicates that Generality Training is effectively interpreting the overall information of the image without bias, ensuring that the model captures the full context of the lesion. In the case of Upsize Training, the Grad-CAM focuses on the primary area of the lesion while still considering the overall lesion as shown in Figure 15b. By learning the main features of the lesion without any specific enhancement, this approach compensates for any information loss in other feature-enhanced models. Unlike the ViT-B/16 model, which processes lesions using patch embeddings, Upsize Training improves the overall performance of the ensemble by concentrating on the most important parts of the lesion. In Figure 15c for the Asymmetry Enhancement Training, we can see that the model concentrates on the shape of the most critical area within the lesion. This helps transmit the core lesion area to the classifier, enabling it to learn and become more sensitive to shape asymmetry. In Figure 15d for the Border Enhancement Training, we can find that the model specifically focuses on and learns from the border of the skin lesion. Since this method blurs the overall color features of the image, it ensures that the model concentrates more on the lesion's contour rather than on color information. This shows that the Border Enhancement Training effectively extracts the border feature rather than color or other characteristics. In Figure 15e for the Color Enhancement Training, we can find that the Grad-CAM focuses mostly on the darkest areas of the lesion, indicating that the Color Enhancement Training intensely focuses on the color aspect of the skin lesion. Thus, it shows that this method allows for effective extraction of color-related features in skin lesions.  
In summary, Grad-CAM allowed us to observe that each of the five different preprocessing methods focuses on different characteristics of the lesion. This suggests that our  
Figure 15: Grad-CAM visualizations for different five blocks: (**a**) Generality Training, (**b**) Up-size Training, (**c**) Asymmetry Enhancement Training, (**d**) Border Enhancement Training, (**e**) Color Enhancement Training.  
model's use of various preprocessing techniques, guided by the ABCD rule, effectively contributed to the improvement in performance.