Mpox Detection

Mpox detection research focuses on developing rapid, accurate, and accessible diagnostic tools to combat outbreaks, particularly in resource-limited settings. Current efforts center on deep learning models, employing architectures like convolutional neural networks (CNNs), transformers (e.g., Swin Transformers), and hybrid approaches, often incorporating transfer learning and data augmentation techniques to address data scarcity. These AI-driven methods aim to improve upon traditional methods by offering faster, cheaper, and more widely deployable solutions for mpox diagnosis, enhancing public health surveillance and response capabilities.