# Enhancing Skin Lesion Classification Performance with the ABC Ensemble Model
## 3 Materials and Methods
### Five Blocks of ABC Ensemble Model
It is 1 if the pixel is within the ROI and 0 otherwise. The classifier for Asymmetry Enhancement Training uses the EfficientNet-B0 model, pre-trained on ImageNet. EfficientNet-B0 is built by stacking MBConv blocks [16], which are optimized using the squeeze-and-excitation method to improve performance by recalculating the weight between channels [46]. Among the EfficientNet family, EfficientNet-B0 is the model with the smallest number of parameters, and it is defined by a width-coefficient of 1.0 and a depth-coefficient of 1.0. **Border Enhancement Training (Block 4):** Border Enhancement Training involves blurring the overall color of an image so that the model does not rely on color features, allowing it to focus more on the edges instead. As depicted in Figure 10, Morphological Transformation applies erosion and dilation to the image data by pooling the image data through a kernel-sized filter. We apply a Morphological Transformation using a kernel size of 6, as shown in Equation (5). This process involves performing erosion on the original image using max pooling. Then, it was expanded again using Equation (6) to resemble the original image [47]. As the erosion and dilation progresses, the overall color of the  
Figure 8: Structure of the Asymmetry Training model (Block 3), including the Segmentation and EfficientNet-B0. Figure 9: Sample segmented images. The image on the left is the original image with ROI extraction applied to it, and the image on the right is the ROI extracted image with segmented mapping applied to it. image becomes unclear and blurred as shown in Figure 11, allowing the model to learn by focusing on the shape and border of the lesion part rather than the color feature. \[E(I)=-max(-I,k), \tag{5}\]  
where \(E(I)\) represents the eroded image obtained by applying the erosion operation on the original image \(I\). \(I\) is the original image that is being processed. \(k\) is the kernel used for the erosion operation. \(k\) defines the shape and size of the neighborhood over which the maximum is calculated. Figure 5:0  
where \(D(E(I))\) represents the dilated image, which is obtained by applying the dilation operation on the eroded image \(E(I)\). \(k\) is the kernel used for the dilation operation. As the classifier of Border Enhancement Training, we use the MobileNetV2 model, pre-trained in Imagenet. The MobileNetV2 model is configured using an Inverted Residual block using bottleneck depth-separable convolution, which can achieve high performance even with low computational requirements [15]. **Color Enhancement Training (Block 5):** Color Enhancement Training involves adjusting the image's color to emphasize the lesion's color over its shape when extracting features  
Figure 5:1 Four different sample images applied Morphological Transformation. The image on the left shows the original image with ROI extraction applied, while the image on the right displays the Morphological Transformation applied to the same image. Figure 5:2 Structure of the Border Training model (Block 4), including the Morphological Transformation and MobileNetV2. from skin lesion images. As illustrated in Figure 12, the image data are preprocessed using a color adjustment technique via the Adjust Hue method, with the results shown in Figure 5:3 In this figure, the lesion's color stands out from the surrounding skin tone, even in datasets where color differences are minimal. This preprocessing helps the model to learn color features more effectively than with the original data [48]. As shown in Equation (7), Adjust Hue converts the existing RGB image data into the HSV color space and normalizes them to a range of [0, 1]. When converting to HSV, a value of [\(-\)0.5, 0.5] is added to the Hue value, adjusting the color tone, and the image is then converted back to RGB format. We set the hue adjustment value to \(h_{f}=-0.1\). As expressed in Equation (8), the color tone adjustment was performed by adding \(h_{f}\) to the value H converted from RGB space to HSV space. \[H=