## 4 Evaluation of Model Performance
### Experimental Setup  
As shown in Figure 9, the proposed algorithm extracts edge, color, and texture features from a single dermoscopic image, and then generates data with reduced dimensionalities smaller than 256 \(\times\) 256, which is the general the data size of a raw image (as shown in Equation (9)). In the generated data, strong feature components shown in the image can be represented with emphasis, and weak feature components can be represented in terms of the number of cumulative pixel intensities, which allows a representation with an aggregated feature component.  
In this study, to perform machine learning on the input feature data extracted from a dermoscopic image, as shown in Figure 10a, we established four convolution groups for the model: Conv-Ag, Conv-Bg, Conv-Cg, and Conv-Dg. As shown in Figure 10b, each convolution group was a modular form with a 1D convolution layer connected to a 1D global max-pooling layer, where N modules were not connected to each other but configured in parallel. In this case, each 1D convolution layer had a different kernel size and stride, and the ReLU function was used as an activation function. Among the convolution groups, the padding of A and C was set as \"same,\" and the padding of B and D was set as \"valid\". For convolution groups A and B, the analysis was performed based on the features of the input data, as shown in Figure 9, whereas for convolution groups C and D, the rows and columns of the input data were transposed to analyze the differences of each feature.  
Figure 9: Example of extracted EFA[\(\cdot\)], CFA[\(\cdot\)], and TFA[\(\cdot\)] for skin lesion classification.  
Each set of data calculated from the convolution groups was concatenated and integrated into one set of data, and after performing learning for each group using fully connected network-1 (FN-1), all data were integrated via a second concatenation. In FN-2, the strong features of the second concatenated data were analyzed. Finally, the data integrated via the third concatenation were classified into the \"softmax\" type.  
In this study, to validate and evaluate the performance of the proposed feature extraction method and learning model, datasets from ISIC 2017 [31] and 2018 [32] were used. In particular, the performance was evaluated based on the annotation information of each set of data obtained from the ISIC datasets. The ISIC 2017 dataset comprised 2000 skin lesion images from three categories: \"nevus (1372)\", \"melanoma (374)\", and \"sebormeic keratosis (254).\" Meanwhile, the ISIC 2018 dataset comprised 10,015 images from seven categories: \"atcinic keratosis (327)\", \"basal cell carcinoma (514)\", \"benign keratosis-like lesions (1099)\", \"dermatofibroma (115)\", \"melanoma (1113)\", \"melanocytic nevi (6705)\", and \"vascular lesions (142).\"  
As for the hardware used in the experimental setup, an Intel Core i7-6700 CPU with 32 GB of RAM and GeForce RTX 2080 Ti were used on a Windows 10 operating system. To implement the feature extraction and machine learning algorithms, Numpy1.18 and OpenCV4.1 were used based on Python3.6, Keras2.1, and Tensorflow2.4 libraries.