# An Identification Method of Feature Interpretation for Melanoma Using Machine Learning
## 1 Introduction
In some studies, color and texture information from dermoscopic images were extracted for melanoma identification, followed by classification using classifiers. [PERSON], [PERSON] et al. [13] proposed a classification framework that compared shape, texture, and color features both locally and holistically to differentiate between melanomas and dysplastic nevi. They utilized SVMs, gradient boosting (GB), and random forest (RF) classifiers, considering single and combined features. During feature extraction, [PERSON], [PERSON] et al. [14] employed color map histogram equalization and a fuzzy system to enhance dermoscopic images, using a genetic algorithm (GA) to optimize extracted texture features for melanoma detection. [PERSON], [PERSON] et al. [15] utilized the Boltzmann entropy method to select fused texture, color, and shape features, and employed SVM for classification. Previous research in this field has concentrated on extracting diverse features like texture, color, and shape to enhance melanoma recognition methods or models. However, few studies have focused on the importance of features and the interpretability of models. For instance, a study by [PERSON], [PERSON] [16] et al. extracted texture features using the GLCM method enhanced by the GLDM technique. However, a detailed analysis of these features was lacking. [PERSON], [PERSON] [17] et al. predominantly extracted color features in the BGR color space, with an opportunity to explore the HSV color space for broader color conditions. Feature selection, as demonstrated by [PERSON], [PERSON] [4] et al., employed the RFE method. However, the focus was on optimal features without a comprehensive selection study. [PERSON] et al. [18] discussed the capacity of the RFE method to rank features. Visualizing the RFE method as a gold standard for encapsulated embedding, as seen in the work by [PERSON] [19], remains limited. The trend leans towards improving the accuracy in melanoma identification methods or models. While accuracy is crucial, model generalizability holds equal significance. Explaining the internal role of model features through visualization can increase the credibility of the model. Moreover, bestowing a degree of generalizability to the model through multifaceted design holds valuable research potential. This paper designs a randomized experiment that employs RFE's feature importance ranking and cross-validation for melanoma identification. The goal is to enhance the credibility of the model and ensure its broader applicability. The process involves preprocessing dermoscopic images to enhance lesion regions and remove impurities. Segmentation using the k-means method extracts lesion regions, followed by cropping to isolate background effects. Three features--color, texture, and shape--are extracted from the lesion region and visualized. RFE is used to filter influential features based on the visualization insights. This approach helps in the understanding of individual feature contributions, explaining the model's performance. Model generalization is ensured through 100-fold randomization and cross-validation during the performance evaluation. The major contributions are as follows:  
1. Preprocessing: employing morphological algorithms, filtering, and image sharpening during preprocessing effectively eliminates noise and artifacts from dermoscopic images. This process enhances image quality and accurately highlights skin lesion regions;
2. Comprehensive Feature Extraction: the method extracts shape features from processed images using techniques like GLMC for texture, color moments for color, and morphology for a holistic representation of lesion attributes;
3. Interpretable Feature Selection: the designed RFE feature selection method employs a 100-fold randomization strategy to derive the feature importance ranking. This ranking concurrently serves to elucidate the individual feature contributions.