Convolutional Neural Networks for Melanoma Screening

Melanoma is the most aggressive and deadliest form of skin cancer, accounting for approximately 75% of skin cancer-related deaths, despite being less common than other skin cancer types. Early diagnosis is critical, as timely intervention—often through minor surgical procedures—can significantly increase patient survival rates. This project presents an automated melanoma screening system that leverages deep learning, specifically a fine-tuned VGG16 Convolutional Neural Network (CNN), to analyze dermoscopic images. The model is trained on a large and diverse dataset from the International Skin Imaging Collaboration (ISIC), enabling it to effectively capture fine-grained features and patterns associated with melanoma. By integrating both lesion localization and classification into a single, streamlined framework, the proposed system enhances diagnostic accuracy and reduces computational overhead. Experimental results demonstrate superior performance compared to traditional multi-stage approaches, highlighting the system’s potential as a reliable, scalable, and clinically applicable tool for early melanoma detection.