Mpox Classifier: A Deep Transfer Learning Model for Monkeypox Disease Detection and Classification

As humans recover from COVID-19, there is a new pandemic threat posed by the monkeypox virus. A daily report of new instances of monkeypox is made, but it is not as deadly or highly contagious as COVID-19. There's a good chance of a worldwide epidemic if nothing is done. Medical imaging is starting to show potential for using deep learning (DL) techniques to identify a patient's ailments. Because a picture has been utilized to learn more about the disease, the human skin infected with the monkeypox virus and the affected skin area may be used to identify the disease early. But as of right now, there isn't a reliable, publicly accessible MonkeyPox database that can be used for training and testing DL models. Furthermore, a suggested and assessed CNN model was the Mpox Classifier, which is an improved DenseNet-201 deep transfer learning model. Based on the initial and enhanced datasets, this research proposes a deep convolutional neural network with 94 and 99.1 percentage accuracy rates, respectively, for properly identifying monkeypox sickness.