## 1 Introduction  
Medical AI for aiding the diagnosis and prediction of diseases has been actively investigated to provide useful information for experts and increase diagnostic accuracy [1, 2, 3]. Many types of medical imaging techniques exist, such as X-ray, computed tomography, and magnetic resonance imaging. Images obtained from medical imaging exhibit high resolution and are suitable for analysis requiring high accuracy and precision. High-resolution images allow minimal feature loss during image preprocessing, such as segmentation and expSansion. In the case of skin lesion images, high-resolution images are effective for diagnosis and provide useful information. In addition, for enhanced visibility, dermatologists use high-quality magnifying dermoscopic lenses and a powerful light sources for in-vivo observations of the skin surface [4, 5]. Skin diseases exhibit similar features to other types of diseases; thus, an observation method as effective as using human eyes is required. This paper presents an observation method and a classification model for the classification of seven types of lesions (actinic keratosis, basal cell carcinoma, benign keratosis-like lesions, dermatofibroma, malignant melanoma, melanocytic nevi, vascular lesions) in various skin conditions. Skin diseases are typically diagnosed during health checkups, where a biopsy is obtained for a more detailed analysis. During a health checkup, a dermatologist visually observes the skin surface using a high-quality dermoscope and performs a diagnosis of the condition via medical consultation. If an abnormal finding is identified during the health checkup, then some or all of the lesions are removed to perform a more precise and detailed examination in a laboratory. The overall process is extremely time-consuming and costly, and the diagnosis result is fault-prone as it depends on the subjective bias of the dermatologist [6]. To address the limitations of human inspection, deep learning-based classification system utilizing big data has been proposed, e.g., ImageNet [7], MSCOCO [8], and PASCAL [9], which demonstrate high performance in terms of the detection and diagnosis of skin lesions [10; 11; 12; 13]. During the visual observation of an object through human eyes, a significant amount of information is transmitted to the brain to allow a rapid recognition of the object [14]. In this process, numerous features of the object, such as its edge, color, and texture, are analyzed; subsequently, the information is described. Furthermore, by conducting an observation closely, a more detailed analysis can be conducted on the features of the object, allowing one to identify and understand the essence of the object. The key to accomplishing such meticulous observation is to obtain numerous features from a single image using a range of feature detectors, followed by combining the extracted features to derive the desired result. [PERSON] et al. [15] extracted 137 features of breast lesions based morphological categories of shape, orientation, margin, echo pattern, and posterior. Subsequently, these extracted features were applied to machine learning algorithms, i.e., AdaBoost, K-nearest neighbors (kNNs), linear discriminant analysis, multilayer perceptron (MLP), radial basis function network, random forest, and support vector machine (SVM), and the results showed a classification performance. [PERSON] et al. [16] proposed a system for recognizing handwritten digits using a combination of different invariant feature extraction methods and multiple classifiers; in particular, cavities, Zernike moments, Hu moments, and the histogram of gradient were used for feature extraction. This study aimed to improve the accuracy of recognizing handwritten digits in the modified National Institute of Standards and Technology database using a Tree, a kNN, an MLP, an SVMOVO, and an SVMOVA. [PERSON] et al. [17] proposed a hybrid approach that combines both feature extraction and feature expansion to improve classification performance based on similarity and metric learning.