## 4 Evaluation of Model Performance
### Experimental Results  
Table 4 shows the experimental results for the optimal learning of the proposed model using the ISIC 2018 dataset. Among 10,015 images, 80% were used as the training dataset (8012 images) and 20% as the test dataset (2003 images). The result was measured using the EarlyStopping() function and the Keras-tuner library. Meanwhile, the performance was evaluated at learning rates of 0.01, 0.001, 0.0001, and 0.00001. The result shows that the  
Figure 10: A convolutional neural network model for learning a skin lesion. (**a**) Overview of a learning model. (**b**) A convolution group structure. AdaGrad function demonstrated the highest accuracy of 0.88 at learning rates of 0.01 and 0.001, and that the loss of the AdaGrad function was insignificant (lr = 0.00001). Figure 11 presents a confusion matrix of the AdaGrad optimization function (lr = 0.001). Table 5 presents the result of the performance comparison between the proposed model and state-of-the-art image classification models. For each of the models compared, the size of the dermoscopic image was normalized to 512 \(\times\) 512, 256 \(\times\) 256, and 128 \(\times\) 128. Meanwhile, for the proposed model, a 1D convolution layer-based model was used with input pattern data of size 50 \(\times\) 256, generated through an analysis of edge, color, and texture features. The performance of the proposed model was compared with those of DenseNet-201, DenseNet-121, SEResNeXt-101, SEResNeXt-50, ResNet-18, ResNet-50, and ResNet-152. The result shows that the accuracy of the proposed method was 88.6%, which was the highest among all the models compared. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Optimization ft.**} & \multicolumn{4}{c}{**Learning Rate (Accuracy/Loss)**} \\ \cline{2-5}  & **0.01** & **0.001** & **0.0001** & **0.00001** \\ \hline SGD & 0.85/0.8517 & 0.86/0.4871 & 0.84/0.4436 & 0.84/0.4436 \\ \hline RMSprop & 0.79/238.2169 & 0.80/14.8006 & 0.83/1.8811 & 0.85/0.8797 \\ \hline Adagrad & _0.88_/0.5842 & _0.88_/0.4543 & 0.85/0.4271 & 0.85/0.4097 \\ \hline Adadelta & 0.86/0.4655 & 0.83/0.4412 & 0.83/0.9728 & 0.79/0.6023 \\ \hline Adam & 0.82/1113.7572 & 0.82/20.2161 & 0.86/0.9728 & 0.86/0.8602 \\ \hline Adamax & 0.82/3.5122 & 0.84/1.2526 & 0.86/0.5242 & 0.86/0.9941 \\ \hline Nadam & 0.82/835.3074 & 0.83/22.9358 & 0.86/0.8873 & 0.84/0.4291 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy and loss of optimization ft. in proposed model on the ISIC 2018 datasets. Figure 11: The Adagrad function (lr = 0.001) confusion matrix using the ISIC 2018 skin lesion datasets. In this study, image processing in pixel units was performed to observe skin lesions more closely. The size of the input data generated by this method was 50 \(\times\) 256, which is smaller than that of other models using data with sizes of 512 \(\times\) 512, 256 \(\times\) 256, and 128 \(\times\) 128 pixels. In addition, despite the small input data size, the proposed method contains essential feature information regarding the edge, color, and texture features; therefore, the proposed method enables a higher-level analysis compared with the processing method using only the original dermoscopic image. Table 6 presents a comparison between methods used in previous studies [33; 34; 35; 36] that achieved an excellent performance in skin lesion classification using the ISIC 2018 dataset and the proposed method. As shown, the proposed method outperformed the other methods. Although the result of Minjie shows the highest accuracy at 89.5%, the proposed method is useful and convenient for feature analysis in pixel units, as the method clearly shows the differences of fine-grained features through the detection of edge, color, and texture features, thereby enhancing the classification performance.