An effective human monkeypox classification using vision transformer
Mohammed Aloraini
Abstract
Abstract Monkeypox virus has become a popular disease as many people have been recently infected with this virus around the world. This virus causes fears of another global pandemic similar to COVID‐19 pandemic. Therefore, it is important to determine infected people at an early stage to reduce the spread of this virus. Creating an automatic approach to detect monkeypox virus is a challenging problem due to the similar appearance between skin infections. Currently, deep convolutional neural network approaches have been used in detecting and classifying skin infections. However, these approaches sever from certain challenges such as being unable to capture the global context and low accuracy rate. In this paper, we propose a novel approach based on vision transformer to automatically detect monkeypox virus in skin images. We fine‐tune vision transformer for the human monkeypox classification problems. This fine‐tuned vision transformer splits an image into image patches and then these patches are used as input to the transformer in a sequence structure, followed by added layers to detect monkeypox virus. The experimental results show that the proposed approach achieves excellent detection accuracy using a public dataset.