  Large language models, notably utilizing Transformer architectures, have
emerged as powerful tools due to their scalability and ability to process large
amounts of data. Dosovitskiy et al. expanded this architecture to introduce
Vision Transformers (ViT), extending its applicability to image processing
tasks. Motivated by this advancement, we fine-tuned two variants of ViT models,
one pre-trained on ImageNet and another trained from scratch, using the NIH
Chest X-ray dataset containing over 100,000 frontal-view X-ray images. Our
study evaluates the performance of these models in the multi-label
classification of 14 distinct diseases, while using Convolutional Neural
Networks (CNNs) and ResNet architectures as baseline models for comparison.
Through rigorous assessment based on accuracy metrics, we identify that the
pre-trained ViT model surpasses CNNs and ResNet in this multilabel
classification task, highlighting its potential for accurate diagnosis of
various lung conditions from chest X-ray images.
