  Monkeypox virus is emerging slowly with the decline of COVID-19 virus
infections around the world. People are afraid of it, thinking that it would
appear as a pandemic like COVID-19. As such, it is crucial to detect them
earlier before widespread community transmission. AI-based detection could help
identify them at the early stage. In this paper, we aim to compare 13 different
pre-trained deep learning (DL) models for the Monkeypox virus detection. For
this, we initially fine-tune them with the addition of universal custom layers
for all of them and analyse the results using four well-established measures:
Precision, Recall, F1-score, and Accuracy. After the identification of the
best-performing DL models, we ensemble them to improve the overall performance
using a majority voting over the probabilistic outputs obtained from them. We
perform our experiments on a publicly available dataset, which results in
average Precision, Recall, F1-score, and Accuracy of 85.44\%, 85.47\%, 85.40\%,
and 87.13\%, respectively with the help of our proposed ensemble approach.
These encouraging results, which outperform the state-of-the-art methods,
suggest that the proposed approach is applicable to health practitioners for
mass screening.
