# Enhancing Skin Lesion Classification Performance with the ABC Ensemble Model
## 3 Materials and Methods
### Five Blocks of ABC Ensemble Model  
This section explains the five blocks, designed to enhance learning by reflecting the characteristics of the skin lesions. After preprocessing to extract the ROI using the U-net model, the data are passed to the five modules. As mentioned in the Section 3.2, our model consists of a total of five blocks: two general blocks and three ABC-related blocks. These five modules consist of Generality Training (Block 1), Upsize Training (Block 2), Asymmetry Enhancement Training (Block 3), Border Enhancement Training (Block 4), and Color Enhancement Training (Block 5). **Generality Training (Block 1):** The Generality Training block represents the most basic model, which does not consider the ABCD rule. Since no specific techniques related to the ABCD rule are applied, this model learns the overall characteristics of the data in terms of general image features. The detailed structure of the model is described in Figure 6. The classifier for the Generality Training block is Vision Transformer-B/16 (ViT-B/16) [21]. The ViT model vectorizes an image of size \(C(Channel)\times H(Height)\times W(Width)\) by cutting  
Figure 5: Four different images applying ROI extraction. The image on the left is the original image, and the image on the right is a sample image with ROI extraction applied. it into \(N\) patches of size \((P,P)\) and flattening them into \(N\)\((1-D)\) vectors of dimension \(P^{2}\times C\). It is a model that extracts features with a transformer encoder consisting of multi-head self-attention and proceeds with classification through a fully connected layer. We also tune the parameters of the Transformer encoder to achieve high performance on the relatively small-sized HAM10000 dataset. **Upsize Training (Block 2):** In Upsize Training, unlike the others, a 384 \(\times\) 384 image is generated through preprocessing and fed into a classifier. The detailed structure of the model is described in Figure 7. By utilizing a larger image size than other inputs, the model can capture finer details and more intricate patterns in the skin lesions, improving feature extraction and learning richer spatial relationships. This allows the model to potentially enhance its performance in detecting subtle irregularities, which may be missed with smaller images. The classifier of the Upsize Training adopts the DenseNet121 model. The DenseNet121 model is a deep learning model developed by connecting feature maps of all layers. In the case of the existing ResNet model, to solve the loss of information between layers, a feature map was added, but in the case of DenseNet, the feature maps are concatenated and the output of the previous layer is used as the input again to extract the result through the Dense block [14]. **Asymmetry Enhancement Training (Block 3):** In Asymmetry Enhancement Training, segmentation is performed to allow the model to focus on asymmetry in the lesion, highlighting the shape of the lesion. As shown in Figure 8, a segmented image generates a segmentation mask of an image using the U-net model for ROI extraction in the preprocessing step. In the generated segmentation mask, as expressed in Equation (4), the segmentation mask part is dot-produced for the original image, and only the part with the segmentation mask of 1 is derived and preprocessed into a segmented image. Through this process, the model can more accurately capture its symmetry, since we can identify the lesion's shape clearly, as shown in Figure 9.  
\[I_{ROI}(x,y)=I(x,y)\cdot B(x,y), \tag{4}\]  
Figure 6: Structure of the Generality Training model (Block 1), including the Vision Transformer (ViT-B/16). Figure 7: Structure of the Upsize Training model (Block 2), including the Upsampling and DenseNet121. where \(I_{ROI}(x,y)\) is the pixel value of the region of interest in the original image. \(I(x,y)\) is the pixel value of the original image at position \((x,y)\). \(B(x,y)\) is the pixel value of the binarized image at position \((x,y)\), which acts as a mask.