Skin Lesion Classification using Machine Learning Algorithm for Differential Diagnosis

Abstract

On comparing diseases that cause major mortality, skin lesions are frequently considered of as minor players in the worldwide league of illness. Melanoma and Melanocytic nevus are skin cancers that have a high fatality rate. In the early stages of skin lesions, accurate classification can help doctors save a patient's life. Even when dermatologists utilize photos to diagnose, specialists' correct diagnosis rates are believed to be 75–84 percent. The purpose of this study is to use machine learning to pre-classify skin lesions as Melanoma or Melanocytic nevus, and to build a decision support system to assist doctors and differential diagnosticians in making better decisions.