Monkeypox Disease Detection with Pretrained Deep Learning Models

Monkeypox has been recognized as the next global pandemic after COVID-19 and its potential damage cannot be neglected. Computer vision-based diagnosis detection method with deep learning models have proven effective during period. However, limited samples, are difficult to full trained. In this paper, twelve CNN-based models, including VGG16, VGG19, ResNet152, DenseNet121, DenseNet201, EfficientNetB7, EfficientNetV2B3, EfficientNetV2M InceptionV3, used for monkeypox skin pictures. Numerical results suggest that DenseNet201 achieves best classification accuracy of 98.89% binary classification, 100% four-class 99.94% six-class over rest models.