Abstract
Musculoskeletal disorders are a significant global health issue. They require precise and efficient analysis of X-ray images for effective diagnosis. Current methods face challenges
in detecting subtle abnormalities, which can delay treatment. This project proposes a comprehensive approach by implementing various Vision Transformers and comparing
their performance with traditional convolutional models like VGG16, VGG19, ResNet, etc. The goal is to identify the most effective model for musculoskeletal X-ray interpretation
through detailed analysis. Using the MURA dataset, the models will be fine-tuned for the specific task of musculoskeletal radiograph analysis. Each model's performance will be
evaluated based on its ability to detect abnormalities accurately. Additional attention is given to interpretability and computational efficiency. The project will assess the
capabilities of different Vision Transformers, including Vit_b32, ViTForImageClassification, and CrossViT, and compare them with established state-of-
the-art models. This detailed comparative analysis aims to recommend the best-performing model for musculoskeletal X-ray interpretation. This ensures that the selected approach provides both accuracy and efficiency while remaining interpretable for healthcare professionals. Future extensions of this work could apply the findings to other medical
imaging tasks and integrate real-time feedback for model improvements.