Early Detection Of Melanoma Disease With AI-Driven Skin Cancer Diagnosis Using Deep Learning Approach

Abstract

Because of its fast growth and high death rate when not identified early, skin cancer, especially melanoma, is a serious health concern. Conventional diagnostic procedures may be laborious and sometimes need the opinion of a dermatologist, who may be out of reach in certain areas. To accurately and early identify melanoma using dermoscopy skin scans, this research suggests an AI-driven diagnostic system that uses deep learning methods. The system is taught to automatically extract and categorize complicated information, differentiating benign lesions from malignant melanoma using Convolutional Neural Networks (CNNs). Accuracy, sensitivity, and specificity are all well achieved by the model, which is trained and verified using publicly accessible datasets like ISIC. This method, which is based on deep learning, helps dermatologists make better clinical decisions, speeds up screening, and drastically decreases the likelihood of human mistakes. By providing a scalable, non-invasive, and effective method for early melanoma detection, the proposed system showcases the power of artificial intelligence to transform skin cancer diagnostics.