Web application for monkeypox disease detection using deep learning

Abstract

The monkeypox virus might become the next big pandemic, like the COVID-19 pandemic, if it is not monitored and controlled correctly. Monkeypox has some similarities to measles and chickenpox, making it very hard to test for it and give a diagnosis in the early stages. A polymerase chain reaction (PCR) test must be used to test for monkeypox properly. This study aims to detect monkeypox accurately using some popular deep-learning models and then compare the results. We used the “Monkeypox Skin Lesion Dataset (MSLD).” Data augmentation has been done to the data to increase the number of images. A web-based prototype application is to be developed where an image can be uploaded, and a prediction will be given if the image is either monkeypox or not. The model used in the web application is the VGG-16 model which identifies monkeypox images with an accuracy of 99%.