Enhance a System for Predicting Skin Lesion Using Hybrid Feature Selection Technique

Visual assessments during medical examinations of skin lesions may be a tough procedure due to the considerable similarity between the lesions. In light of the increasing prevalence of skin cancer and limited clinical competence, it is imperative to develop artificial intelligence (AI)-powered tools for early-stage diagnosis of skin cancer. Given the availability of extensive skin lesion datasets in scientific literature, AI-powered deep learning (DL) models have shown effective in distinguishing between cancerous and benign skin lesions utilizing dermoscopic pictures. Early identification of skin cancer may lead to a lower mortality rate. Dermoscopy is a very efficient method for identifying and categorizing skin cancer. The research used mathematical-based hybrid methodologies to discover essential characteristics. The methodologies used for our investigation include Chi-square, information gain, and principal component analysis. The findings of this investigation are very comprehensive and meticulous. The validation approach included examining the variations in parameter settings across many machine learning algorithms.