Innovative Approaches to Skin Cancer Diagnosis: Leveraging Machine Learning and Big Data for Enhanced Detection

Abstract

Treatment and early detection advances in skin cancer have become possible through machine learning techniques within big data frameworks. The review discusses the implementation of machine learning technology through supervised learning and deep learning and ensemble methods together with big data contributions for improving skin cancer diagnosis accuracy. The analysis of large datasets containing dermatology image collections and medical records and genetic information through these methods delivers quicker and more accurate results than conventional diagnostic practices. This research analyses implementation hurdles of these technologies while addressing issues concerning data privacy together with interpretability and model bias. The paper concludes through an assessment of future prospects which highlight how machine learning integrated with big data carries the potential to transform skin cancer diagnostic practices along with treatments and individualized medical strategies.