Abstract :

Skin cancer is one of the most commonly diagnosed cancers worldwide, and its incidence continues to increase every year. Early detection of skin cancer significantly improves treatment success and patient survival rates. Traditional diagnosis relies on visual examination and biopsy, which are time-consuming and dependent on expert dermatologists. In many regions, access to specialized dermatological care is limited. Deep learning offers an automated and accurate solution for skin cancer classification using medical images. Convolutional Neural Networks (CNNs) can learn complex patterns from dermoscopic images. These models analyze texture, color, shape, and lesion boundaries to distinguish between benign and malignant skin lesions. Automated classification reduces diagnostic errors and variability. Large annotated datasets enable robust model training. Transfer learning improves performance with limited medical data. Deep learning systems can achieve dermatologist-level accuracy. Real-