Mpox Close Skin Images

The <strong>Mpox Close Skin Images</strong> dataset (<strong>MCSI</strong>) 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.<br> <br> It includes a total of 400 pictures homogeneously divided in 4 different classes: <em>mpox</em>, which contains samples of mpox (formerly Monkeypox) skin lesions; <em>chickenpox</em>, with samples of chickenpox cases; <em>acne</em>, containing samples of acne at different severity levels; and <em>healthy</em>, which contains samples of skin without any evident symptoms.<br> <br> This repository is part of the supplementary material accompanying the paper named: <em>A Transfer Learning and Explainable Solution to Detect mpox from Smartphones images</em>.<br> <br> Please, refer to the <em>README.md</em> file for more details.<br> <br> <strong>Version 2</strong> includes the addition of the file <em>skin_tone_labels.csv</em>. 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.