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