Skin Cancer

Skin cancer research focuses on developing accurate and efficient diagnostic tools, primarily leveraging deep learning models for image analysis. Current efforts concentrate on improving the performance and efficiency of various architectures, including convolutional neural networks (CNNs), vision transformers (ViTs), and hybrid approaches, often incorporating techniques like knowledge distillation and attention mechanisms to enhance accuracy while minimizing computational demands. These advancements aim to improve early detection and classification of skin lesions, ultimately leading to better patient outcomes and potentially reducing the burden on healthcare systems through faster and more accessible diagnostics.