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.

Mpox Close Skin Images / M.G. Campana, M. Colussi, F. Delmastro, S. Mascetti, E. Pagani. - (2023 May 18). [10.5281/zenodo.7948350]

Mpox Close Skin Images

M. Colussi
Secondo
;
S. Mascetti
Penultimo
;
E. Pagani
Ultimo
2023

Abstract

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.
18-mag-2023
Settore INF/01 - Informatica
Settore MED/17 - Malattie Infettive
https://zenodo.org/record/7948350
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/974731
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