Monkeypox Diagnostic-Aid System with Skin Images Using Convolutional Neural Networks

Background: Monkeypox is similar to classical smallpox and, until now, isolated cases have been detected in Africa, but in 2022 it has spread around the world and was declared an international health emergency by the WHO at the end of July.Objective: In this work, we collect monkeypox images from official government websites and public datasets (as well as images of healthy people and people with other skin diseases) and create the MonkeypoxSkin dataset (publicly available), which contains close-up images of the skin where you can see the posthulas formed by the disease. With these images, we design, implement, and evaluate several diagnostic aid systems for monkeypox disease.Methods: The classifiers developed and evaluated are based on Convolutional Neural Networks models and some ensembles composed of a combination of those models, obtaining automatic classification results between healthy, monkeypox and other skin damages, given a close skin tissue image.Results: The results show a system accuracy greater than 93% when using a unique CNN model (VGG-19 and ResNet50), and greater than 98% when using a CNN ensemble formed by ResNet50, EfficientNet-B0 and MobileNet-V2.Conclusions: In contrast to the work found to date, this classifier focusses on disease classification based on a low-resolution image obtained from the skin surface, so it can be integrated into a mobile application for mass screening. To do this, it has been necessary to design a proprietary dataset that is publicly available for use. Furthermore, the results obtained improve the accuracy of the previous works.