Published Paper Details:

Monkeypox is caused by the Zoonotic Orthopoxvirus, which is related to both cowpox and smallpox and is a member of the Poxviridae family. (a member of the genus Orthopoxvirus). It is mostly spread by primates and rodents, but human-to-human transfer is also prevalent. Although the Monkeypox virus is not as lethal or infectious as COVID-19, new instances are recorded on a daily basis from all over the globe. As a result, it will come as no surprise if the world experiences another global epidemic due to a dearth of effective preventative measures. As a consequence, because the Monkeypox-related disease infected human skin, an image of the diseased skin can be recorded and used to further diagnosis the illness. We use the "Monkeypox2022" dataset, which is open source and can be obtained from the GitHub repository. Data collection and data enhancement techniques will be used. Deep Convolution Neural Network and VGG-16 is the method we are employing. Our concept is simple to execute because it is both cost-effective and time effective. There is no need for extensive PCR or microscopy work. As a result, our proposed model could be used to evaluate real-time monitoring of people for Monkeypox pox symptoms. In this case, we want to assess temporal complexity, sensitivity, and true positive rate, as well as accuracy.