  The recent development of Monkeypox disease among various nations poses a
global pandemic threat when the world is still fighting Coronavirus
Disease-2019 (COVID-19). At its dawn, the slow and steady transmission of
Monkeypox disease among individuals needs to be addressed seriously. Over the
years, Deep learning (DL) based disease prediction has demonstrated true
potential by providing early, cheap, and affordable diagnosis facilities.
Considering this opportunity, we have conducted two studies where we modified
and tested six distinct deep learning models-VGG16, InceptionResNetV2,
ResNet50, ResNet101, MobileNetV2, and VGG19-using transfer learning approaches.
Our preliminary computational results show that the proposed modified
InceptionResNetV2 and MobileNetV2 models perform best by achieving an accuracy
ranging from 93% to 99%. Our findings are reinforced by recent academic work
that demonstrates improved performance in constructing multiple disease
diagnosis models using transfer learning approaches. Lastly, we further explain
our model prediction using Local Interpretable Model-Agnostic Explanations
(LIME), which play an essential role in identifying important features that
characterize the onset of Monkeypox disease.
