Breast Cancer (BC) is a leading cause of mortality among women worldwide and early detection remains crucial for improving patient survival rates. Thermography offers a non-invasive imaging technique for identifying abnormal temperature patterns associated with malignancies. In this work, we propose a video transformer-based model for automatic BC classification from thermographic images using Universidade Federal Fluminense dynamic acquisition protocol. Our model achieved an Area Under the Receiver Operating Characteristic Curve score of 0.94 on a test set of 15 patients outperforming the results of the state-of-the-art set by 3D Convolutional Neural Networks.