The Mpox Close Skin Images dataset (MCSI) is a collection of skin images obtained from diverse public sources, that we accurately pre-processed (i.e., cropped and zoomed) in order to focus the skin lesion (if present), and to evaluate Machine Learning models aimed at detecting different pathologies from skin lesion pictures taken with smartphone cameras. It includes a total of 400 pictures homogeneously divided in 4 different classes: mpox, which contains samples of mpox (formerly Monkeypox) skin lesions; chickenpox, with samples of chickenpox cases; acne, containing samples of acne at different severity levels; and healthy, which contains samples of skin without any evident symptoms. This repository is part of the supplementary material accompanying the paper named: A Transfer Learning and Explainable Solution to Detect mpox from Smartphones images. Please, refer to the README.md file for more details. Version 2 includes the addition of the file skin_tone_labels.csv. This file comprises the classification of skin images into Light and Dark skin tone classes. These classifications are instrumental in assessing the model's prediction fairness across various skin pigments.

This work was produced with the co-funding European Union - Next Generation EU, in the context of The National Recovery and Resilience Plan. The funding derives partially from Investment 1.5 Ecosystems of Innovation, Project Tuscany Health Ecosystem (THE), CUP: B83C22003920001 in which the authors M. G. Campana and F. Delmastro are involved, from Project MUSA – Multilayered Urban Sustainability Action in the Investment 1.5 Ecosystems of Innovation in which the author S. Mascetti is involved, and from the Research and Innovation Program PE00000014, "SEcurity and RIghts in the CyberSpace (SERICS)", CUP J33C22002810001, in which the author E. Pagani is involved.