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The recent development of Monkeypox disease among various nations poses aglobal pandemic threat when the world is still fighting CoronavirusDisease-2019 (COVID-19). At its dawn, the slow and steady transmission ofMonkeypox disease among individuals needs to be addressed seriously. Over theyears, Deep learning (DL) based disease prediction has demonstrated truepotential by providing early, cheap, and affordable diagnosis facilities.Considering this opportunity, we have conducted two studies where we modifiedand 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 modifiedInceptionResNetV2 and MobileNetV2 models perform best by achieving an accuracyranging from 93% to 99%. Our findings are reinforced by recent academic workthat demonstrates improved performance in constructing multiple diseasediagnosis models using transfer learning approaches. Lastly, we further explainour model prediction using Local Interpretable Model-Agnostic Explanations(LIME), which play an essential role in identifying important features thatcharacterize the onset of Monkeypox disease.

Transfer learning and Local interpretable model agnostic based visual  approach in Monkeypox Disease Detection and Classification: A Deep Learning  insights