## 5 Conclusions  
For the classification of skin lesions in dermoscopic images, we herein proposed a detailed feature extraction method that analyzes edge, color, and texture features in pixel units. The proposed method applies the line-segment-type analysis algorithm to present the line-segment information of edges based on different types. Additionally, the type of visual lines was analyzed in pixel units, and line-segment features were extracted based on cumulative statistics for the same intensity. Regarding color feature extraction for detecting fine-grained changes in color for skin lesions, the image was converted into RGB, HSV, and YCbCr color spaces to analyze the histogram distribution of each channel. Furthermore, to extract texture features, an LTEM filter was used to extract 25 different elements of texture information, and the features were extracted using histogram analysis and a line-segment-type analysis algorithm. Using the input data generated through this process, machine learning was performed via a learning model designed based on a 1D convolution layer. In the designed model, 1D convolution layers with different learning parameters were connected in parallel to determine the optimal weights for the input data.  
The performance of the proposed feature data generation method and learning model was compared and evaluated with other well-known classification models using the ISIC 2017 and ISIC 2018 datasets. In experiments using the ISIC 2018 dataset, the proposed method showed a superior performance with an accuracy of 94.9%, compared to previous research results. On the other hand, for the recognition of melanoma and seborrheic keratosis using the ISIC 2017 dataset, the proposed method showed a comparable performance of 97.1% with the other models. Additionally, when compared to results reported in the literature with excellent performance, the proposed method demonstrated an almost equivalent performance.  
The method proposed herein can detect fine-grained variations in skin lesion features, and in situations requiring the learning of numerous features, since the proposed method uses extracted data, a reduced computational load can be expected as compared with models using an entire image as the input. However, a significant amount of feature extraction is required for detecting regions of skin lesion with fine-grained features. To effectively extract numerous features, the feature extraction process must be further simplified to increase the speed of data preprocessing and model training. In addition, the experiment revealed that there is a possibility of losing significant feature information during the process of normalizing image sizes, which may result in reduced performance. Therefore, additional experiments are needed to address this issue. The proposed feature extraction method and learning model for detecting and classifying skin lesions may serve as a novel approach for deep learning-based image recognition. Furthermore, they may yield an excellent performance in other fields that require the detection of fine-grained visual differences in input images.  
Investigation, [PERSON], [PERSON] ([PERSON]), [PERSON] ([PERSON]); writing--original draft preparation, C.K.; writing--review and editing, W.L. All authors have read and agreed to the published version of the manuscript. This research was supported by Sangji University Research Fund, 2020.  
Not applicable.  
The authors declare no conflict of interest.