The human simian virus, also known as monkeypox, is transmitted from animals to humans. It is a viral infection that is spreading and has already infected thousands of people worldwide. Thus, the objective of this study was to implement a classification model for Mpox from images with skin lesions using a vision transformer and convolutional neural networks. A methodology based on 5 phases was applied: Obtaining dataset; Preprocessing (Cutting, enlarging, resizing, and normalizing the images); Implementation of the models (Vision Transformer, Data-efficient image Transformer, Pooling-based Vision Transformer, Swin Transformer, MobileVit, Convolutions and Transformer, and Tokens to Token ViT); Model evaluation (Accuracy, Precision, Recall and F1-Score) and Prediction. The superior results were obtained with the Vision Transformer model with the metrics Accuracy, Precision, recall and F1-Score, whose results were 99.80%, 100%, 99. 60%, and 99.80% respectively, which demonstrates the high capacity of deep learning models to extract different complex characteristics automatically and efficiently, which highlights them as sufficiently robust tools to be considered in clinical applications.