Skin Cancer Detection-Technological Innovation in Personalized Risk Assessment and Early Warning System and Empowering Healthcare Providers

Abstract

Skin cancer is one of the most deadly cancers and a major cause of global mortality. However, early detection can greatly lower death rates. Traditionally, skin cancer is identified through visual examination, which can sometimes be inaccurate and miss subtle early indications. Early and precise diagnosis plays a key role in enhancing patient outcomes and preventing the progression of skin cancer. Recently, deep learning techniques have shown promise in assisting dermatologists with accurate diagnoses at earlier stages. Despite these advancements in dermatology, challenges in achieving quick and accurate diagnoses of skin cancer remain. The integration of 3D imaging with Texture-Based Processing (3D TBP) represents a cutting-edge approach to skin cancer detection. By analyzing texture and patterns within 3D images, advanced algorithms can identify slight texture variations that may indicate cancerous changes—variations that are often missed in traditional 2D analysis.