Melanoma Detection

Melanoma detection research focuses on developing accurate and efficient automated systems for early diagnosis, improving patient outcomes. Current efforts concentrate on refining deep learning models, including convolutional neural networks (like ResNet, EfficientNet, and CoAtNet) and hybrid architectures combining segmentation (e.g., U-Net, SegNet) and classification capabilities, often leveraging techniques like knowledge distillation and self-supervised learning to enhance performance and reduce computational demands. These advancements aim to improve diagnostic accuracy, potentially surpassing human performance in certain aspects, and facilitate wider access to timely and reliable melanoma screening through web-based and portable applications.