Skin Cancer Detection Using Machine Learning

Skin cancer is one of the most common and rapidly growing cancers globally, requiring early diagnosis for effective treatment. Traditional visual examination by dermatologists is time-consuming and subjective. This paper proposes a deep learning–based multi-class skin cancer detection model using Convolutional Neural Networks (CNNs) trained on dermoscopic images. The model accurately classifies different types of skin lesions, including melanoma, basal cell carcinoma, squamous cell carcinoma, and benign nevi. The methodology includes preprocessing, data augmentation, feature extraction, and classification using transfer learning (VGG16 / ResNet50). Experimental results demonstrate high accuracy and reliability, proving the potential of AI-assisted systems in early diagnosis of skin cancer.