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While the world is still attempting to recover from the damage caused by thebroad spread of COVID-19, the Monkeypox virus poses a new threat of becoming aglobal pandemic. Although the Monkeypox virus itself is not deadly andcontagious as COVID-19, still every day, new patients case has been reportedfrom many nations. Therefore, it will be no surprise if the world ever facesanother global pandemic due to the lack of proper precautious steps. Recently,Machine learning (ML) has demonstrated huge potential in image-based diagnosessuch as cancer detection, tumor cell identification, and COVID-19 patientdetection. Therefore, a similar application can be adopted to diagnose theMonkeypox-related disease as it infected the human skin, which image can beacquired and further used in diagnosing the disease. Considering thisopportunity, in this work, we introduce a newly developed "Monkeypox2022"dataset that is publicly available to use and can be obtained from our sharedGitHub repository. The dataset is created by collecting images from multipleopen-source and online portals that do not impose any restrictions on use, evenfor commercial purposes, hence giving a safer path to use and disseminate suchdata when constructing and deploying any type of ML model. Further, we proposeand evaluate a modified VGG16 model, which includes two distinct studies: StudyOne and Two. Our exploratory computational results indicate that our suggestedmodel 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 extractionutilizing Local Interpretable Model-Agnostic Explanations (LIME) help to adeeper insight into specific features that characterize the onset of theMonkeypox virus.

Image Data collection and implementation of deep learning-based model in detecting Monkeypox disease using modified VGG16