  While the world is still attempting to recover from the damage caused by the
broad spread of COVID-19, the Monkeypox virus poses a new threat of becoming a
global pandemic. Although the Monkeypox virus itself is not deadly and
contagious as COVID-19, still every day, new patients case has been reported
from many nations. Therefore, it will be no surprise if the world ever faces
another global pandemic due to the lack of proper precautious steps. Recently,
Machine learning (ML) has demonstrated huge potential in image-based diagnoses
such as cancer detection, tumor cell identification, and COVID-19 patient
detection. Therefore, a similar application can be adopted to diagnose the
Monkeypox-related disease as it infected the human skin, which image can be
acquired and further used in diagnosing the disease. Considering this
opportunity, in this work, we introduce a newly developed "Monkeypox2022"
dataset that is publicly available to use and can be obtained from our shared
GitHub repository. The dataset is created by collecting images from multiple
open-source and online portals that do not impose any restrictions on use, even
for commercial purposes, hence giving a safer path to use and disseminate such
data when constructing and deploying any type of ML model. Further, we propose
and evaluate a modified VGG16 model, which includes two distinct studies: Study
One and Two. Our exploratory computational results indicate that our suggested
model can identify Monkeypox patients with an accuracy of $97\pm1.8\%$
(AUC=97.2) and $88\pm0.8\%$ (AUC=0.867) for Study One and Two, respectively.
Additionally, we explain our model's prediction and feature extraction
utilizing Local Interpretable Model-Agnostic Explanations (LIME) help to a
deeper insight into specific features that characterize the onset of the
Monkeypox virus.
