Automated Classification of Monkeypox Disease based on Deep Learning

Monkeypox virus is widely spreading across the world at a rapid rate, after the aftermath of COVID-19 disease. It is spread via body contact, droplets, or mucusof the eye, nose, or mouth. Some of the early-stage symptoms of patients are fever, aches in body or fatigue or red bump on the skin. Monkeypox is a disease from the genus Orthopoxvirus. It closely resembles chickenpox, smallpox and measles regarding clinical features. The minor variations in rashes on the skin, coupled with the relative rareness of monkeypox have made the diagnosis of this condition at early stage challenging for healthcare professionals. The symptoms will last for 2 to 4 weeks. In this paper, Monkeypox Skin Image Dataset is used to study the efficiency using Convolution Neural Network classification models on the images of skin for Monkeypox detection among smallpox, chickenpox and measles. This study found that ResNet-50 model has great potential in the detection of Monkeypox disease.