## 1 Introduction
Feature extraction and expansion were performed using training samples obtained from data transformation and a set of standard distances. The method was used for preprocessing input data into an SVM, and the performance of the proposed method was demonstrated via four different evaluation experiments using face and object recognition data. Based on previous studies, meticulous observations performed via machine learning typically involves the use of two or more feature detectors to extract different feature information. However, these previous studies primarily adopted hybrid approaches or systems with combined or integrated feature detector models. In this case, the throughput of the detectors and machine learning models must be high; as such, sophisticated equipment and high energy are required. The features derived from the models may be difficult to analyze depending on the detector used, and the hyperparameters of all detectors must be analyzed, which is often challenging. In general, since hyperparameters affect the final results and performance, they can only be tuned optimally by performing a series of experiments and time-consuming process. In this paper, we focused on the basic components of images through relatively simple and easy feature analysis instead of using multiple detectors or performing large-scale models and complex processing as in previous studies [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17]. These components are essential features used in the diagnosis of skin lesions, and we aimed to extract them to reduce the scale and complexity of neural models. In addition, the size of skin lesion images can always be reduced to small, fixed-size data for clear representation of the processing. Shape features based on edge information allow the analysis of the visual appearance of skin lesions, color features allow the observation of skin color changes due to disease, and texture features allow the detection of surface abnormalities of the skin caused by disease. To extract these various types of feature information in this study, shape, color, and texture features were extracted using an edge detector. Subsequently, color space conversion was performed and [PERSON]'s texture energy measure (LTEM) texture detection filter [18] was used to augment the features. Consequently, 16 edge features were generated to extract the shape information of skin lesions, and nine feature vectors were generated for color features through color space (RGB, HSV, and YCrCb) [19; 20; 21] conversion and histogram analysis for each channel of the respective color space. In addition, by obtaining 25 different texture features using the LTEM filter, 50 visual features were generated to be used for model training. In this case, to reduce the computational load required when a single dermoscopic image was augmented into 50 features, the augmented 50 visual features were converted into structured data via line-segment-type analysis [22; 23] and histogram analysis. The converted data were used as inputs of a deep learning model for skin lesion classification, and experiments were conducted on feature augmentation and classification models. This paper introduces existing studies on skin lesion classification in Section 2, describes feature extraction and diagnosis process in Section 3, presents the performance evaluation of the proposed method in Section 4, and finally concludes the paper with Section 5.