Skin Cancer Detection

Skin cancer detection research focuses on developing accurate and efficient automated diagnostic tools using deep learning, primarily employing convolutional neural networks (CNNs), vision transformers (ViTs), and hybrid architectures combining segmentation and classification models. Current efforts emphasize improving model explainability, addressing biases related to skin tone and disease prevalence in training data, and optimizing models for resource-constrained environments like mobile devices. These advancements aim to improve early diagnosis, reduce healthcare disparities, and ultimately enhance patient outcomes by providing faster, more accessible, and equitable skin cancer screening.