# An Identification Method of Feature Interpretation for Melanoma Using Machine Learning
## 1 Introduction  
Skin diseases encompass a wide range of conditions, some of which can progress into severe and fatal skin cancers. The World Health Organization projects that there will be approximately 2.2 million cases of skin cancer by 2025 [1]. Among skin cancers, melanoma stands out due to its very high mortality rate; its prevalence continues to rise, with over 130,000 new cases diagnosed [2]. Research has established a strong link between exposure to sunlight and ultraviolet radiation and the development of melanoma. Prolonged exposure to ultraviolet radiation, along with cellular mutations or genetic defects, can lead to the mutation and rapid proliferation of normal melanocytes in the basal layer of the epidermis, culminating in the progression to melanoma. Due to its aggressive nature and high mutation frequency, early diagnosis of melanoma is of utmost importance. At the initial stage, the success rate of cure can exceed 90% [3]. For the accurate early diagnosis of skin diseases, dermoscopic images are often employed. Dermoscopy, a non-invasive imaging technique, provides a magnified view of the deeper layers of the skin under non-invasive conditions, facilitating the analysis and diagnosis of skin lesions and affected areas [4]. The utilization of dermoscopic images enhances medical observation, significantly improving the diagnosis of various skin diseases. Image processing for the detection, segmentation, and classification of skin lesions encounters various difficulties [5], including: (a) the presence of noise and artifacts such as hairs, bubbles, and blood vessels; (b) irregular, random, and sometimes diffuse edges with low contrast between the lesion and healthy skin; (c) illumination malfunctions; and (d) variability in image characteristics due to different types of capturing equipment. Due to these challenges, image preprocessing is essential before diagnosing melanoma. In recent years, the rapid advancement of artificial intelligence and machine learning technologies has garnered significant attention from researchers, leading to the development of automated image processing systems for diagnosing diseases in the medical field. Various image CAD systems have been developed for the diagnostic classification of melanoma, providing valuable assistance to physicians [6]. The CAD system of melanoma based on machine learning typically involves three steps:  
1. Segmentation of the skin lesion region;
2. Feature extraction from the skin lesion region;
3. Classification of the extracted features using a classifier. To achieve more accurate classification results, segmentation is crucial, as it provides precise samples for the classifier. While numerous studies have focused on modifying CAD systems for melanoma classification, a limited number have analyzed the features extracted from melanoma and explored their link with the pathological perspective. Dermoscopic images can pose challenges for identification due to hair problems or acquisition processes, but these issues can be addressed through image preprocessing. [PERSON], [PERSON] et al. [7] demonstrated that preprocessing significantly impacts the segmentation process of skin lesion areas, leading to more accurate results. Proper segmentation is crucial as it affects the subsequent feature extraction and melanoma identification, making preprocessing necessary. [PERSON], [PERSON] et al. [8] and [PERSON], [PERSON] et al. [9] resized images and employed simple filtering to address hair effects and noise elimination. The preprocessing stage primarily aims to improve image quality without altering the size of the skin lesion region. Segmentation can also be integrated into the preprocessing stage, utilizing traditional algorithms like adaptive threshold segmentation [10] and [PERSON]'s clustering-based approach [11]. Automated CAD systems are commonly based on various feature descriptions, including color, texture, and shape [12].