Melanoma Classification

Melanoma classification research focuses on developing accurate and efficient automated systems for identifying melanomas from dermoscopic and histopathological images, aiding early diagnosis and improving patient outcomes. Current efforts concentrate on refining deep learning models, including convolutional neural networks (CNNs), transformers, and variations of the Segment Anything Model (SAM), often incorporating techniques like transfer learning, data augmentation, and attention mechanisms to enhance performance and address challenges such as data imbalance and domain shift. These advancements aim to improve diagnostic accuracy, potentially reducing reliance on solely human expertise and enabling faster, more accessible melanoma detection. Furthermore, research emphasizes improving model interpretability and robustness to ensure clinical acceptance and reliable performance in diverse real-world settings.