Vision Transformer based Deep Learning Model for Monkeypox Detection

Abstract

Images of skin lesions may be used to detect this virus, which is a reliable method for identifying the pox virus group. However, early identification and prediction are difficult due to the virus’s resemblance to other pox viruses. An intelligent computer-aided detection system may be a great alternative to relying on labor-intensive human identification. Therefore, in this research an machine learning and deep learning classification method for monkeypox prediction has been proposed and trained, and tested over 1300 skin lesion images. A comparative analysis of machine learning algorithms (K-NN and SVM) and Deep learning algorithms (Vision Transformer, RestNet50) to establish the efficacy of this study. Layered Convolutional Neural Network (CNN) with transfer learning and pretrained models such as RestNet50 integrated, together with customized hyperparameters for extracting the features from the input images. The feed-forward, which is also completely integrated, helped the algorithm divide the visuals into two categories–chickenpox and monkeypox. Among the ML model, the K-NN achieves the best accuracy of 84%. However, The Vision Transformer(ViT) outperforms the other models with an accuracy of 93%. In Addition to it, we analyze our pretrained model to achieve the desired outcome based on the relevant existing model as already established to the end user.