Skin Lesion Segmentation Using Adaptive Color Segmentation and Decision Tree
A decision tree is a machine learning prediction model that uses a hierarchical set of rules for decision making. The decision flow is described in the form of a tree structure, where each node represents a decision or choice, while its branches reflect the consequences of each decision. Decision Trees are an effective tool for solving classification and regression problems. They provide high interpretability by visually describing the relationship between input and output variables. Color image segmentation is an important pre-processing step in a wide range of image processing applications. The segmentation of skin lesions is particularly important in image analysis, especially for the classification of lesions in dermoscopy images. Image segmentation is the process of grouping pixels in an image into homogeneous regions based on properties such as color, texture, and exposure. The goal of this process is the separation of the region of interest from the healthy region. In threshold-based segmentation, the success of image segmentation depends primarily on selecting the optimal threshold