Today, due to the problem of environmental pollution, water, and other factors have caused many dangerous diseases, including cancer. According to recent statistics, breast cancer is one of the leading diseases in women, and this disease tends to increase more and more. To detect and diagnose the disease, doctors perform many examinations: self-examination, clinical examina- tion, X-ray, ultrasound screening, etc., in which X-ray is a highly e®ective method. This study proposes an approach to detecting and classifying breast cancer on an X-ray dataset using a re ̄ned Vision Transformer (ViT), ViT-B32. The considered dataset contains about 7000 X-ray images from patients aged 27 to 90, labeled as malignant, benign, or normal. As presented in scenarios, the study yielded positive results, with 91% to 94% in ACC and F1-score metrics. Furthermore, it has shown that the results obtained for breast cancer detection on X-ray images using the ̄ne-tuned ViT architecture outperformed CNN models such as VGG16, MobileNet, Xception, ResNet50, and some state-of-the-art approaches.