Monkeypox Disease Detection

Monkeypox disease detection research focuses on developing rapid and accurate diagnostic tools, primarily using image-based analysis of skin lesions to overcome limitations of traditional methods. Current efforts center on deep learning models, employing architectures like CNNs (including variations such as ResNet, Inception, and EfficientNet), Swin Transformers, and hybrid approaches that combine these techniques with attention mechanisms to improve feature extraction and classification accuracy. These advancements aim to provide healthcare professionals with efficient, automated tools for early and reliable monkeypox diagnosis, aiding in public health surveillance and response efforts.