In this paper, we propose a novel ensemble method, termed Bagged Vision Transformers (BagViT), to enhance the classification accuracy for Chest X-ray (CXR) images. BagViT constructs an ensemble of independent Vision Transformer (ViT) models, each of which is trained on a bootstrap sample (sampling with replacement) drawn from the original training dataset. To enhance model diversity, we use MixUp to generate synthetic training examples and introduce training randomness by varying the number of training epochs and selectively fine-tuning the top layers of each model. Final predictions are obtained through majority voting. Experimental results on a real-world dataset collected from Chau Doc Hospital (An Giang, Vietnam) demonstrate that BagViT significantly outperforms fine-tuned baselines such as VGG16, ResNet, DenseNet, ViT. Our BagViT achieves a classification accuracy of 72.25%, highlighting the effectiveness of ensemble learning with transformer architectures in scenarios with complex CXR images.