  Monkeypox is a rare disease that raised concern among medical specialists
following the convi-19 pandemic. It's concerning since monkeypox is difficult
to diagnose early on because of symptoms that are similar to chickenpox and
measles. Furthermore, because this is a rare condition, there is a knowledge
gap among healthcare professionals. As a result, there is an urgent need for a
novel technique to combat and anticipate the disease in the early phases of
individual virus infection. Multiple CNN-based pre-trained models, including
VGG-16, VGG-19, Restnet50, Inception-V3, Densnet, Xception, MobileNetV2,
Alexnet, Lenet, and majority Voting, were employed in classification in this
study. For this study, multiple data sets were combined, such as monkeypox vs
chickenpox, monkeypox versus measles, monkeypox versus normal, and monkeypox
versus all diseases. Majority voting performed 97% in monkeypox vs chickenpox,
Xception achieved 79% in monkeypox against measles, MobileNetV2 scored 96% in
monkeypox vs normal, and Lenet performed 80% in monkeypox versus all.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning