Skin Cancer Classification

Skin cancer classification research aims to develop accurate and efficient automated systems for diagnosing skin lesions, improving early detection and treatment outcomes. Current efforts focus on leveraging deep learning architectures, such as convolutional neural networks (CNNs), Vision Transformers, and Siamese networks, often incorporating techniques like transfer learning, data augmentation, and attention mechanisms to address class imbalance and improve diagnostic accuracy. These advancements hold significant promise for improving the accessibility and efficiency of skin cancer diagnosis, particularly in resource-constrained settings, and for aiding dermatologists in making more informed decisions.