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In recent months, the monkeypox (mpox) virus -- previously endemic in alimited area of the world -- has started spreading in multiple countries untilbeing declared a ``public health emergency of international concern'' by theWorld Health Organization. The alert was renewed in February 2023 due to apersisting sustained incidence of the virus in several countries and worriesabout possible new outbreaks. Low-income countries with inadequateinfrastructures for vaccine and testing administration are particularly atrisk.  A symptom of mpox infection is the appearance of skin rashes and eruptions,which can drive people to seek medical advice. A technology that might helpperform a preliminary screening based on the aspect of skin lesions is the useof Machine Learning for image classification. However, to make this technologysuitable on a large scale, it should be usable directly on mobile devices ofpeople, with a possible notification to a remote medical expert.  In this work, we investigate the adoption of Deep Learning to detect mpoxfrom skin lesion images. The proposal leverages Transfer Learning to cope withthe scarce availability of mpox image datasets. As a first step, a homogenous,unpolluted, dataset is produced by manual selection and preprocessing ofavailable image data. It will also be released publicly to researchers in thefield. Then, a thorough comparison is conducted amongst several ConvolutionalNeural Networks, based on a 10-fold stratified cross-validation. The bestmodels are then optimized through quantization for use on mobile devices;measures of classification quality, memory footprint, and processing timesvalidate the feasibility of our proposal. Additionally, the use of eXplainableAI is investigated as a suitable instrument to both technically and clinicallyvalidate classification outcomes.

A Transfer Learning and Explainable Solution to Detect mpox from Smartphones images