Using Swin Transformer and SIFT Algorithm to Detect Bone Abnormalities

This research paper investigates the combination of cutting-edge deep learning architectures, specifically the Swin Transformer, with conventional feature extraction methods, such as the Scale-Invariant Feature Transform (SIFT) algorithm, for the identification and classification of fractures. We seek to improve the accuracy and robustness of fracture identification from medical pictures by integrating characteristics acquired from SIFT with the capabilities of Swin Transformer during a 16- week period, we show the efficacy of our hybrid technique through thorough experimentation and evaluation, providing insights into its performance in comparison to traditional methods. Our research advances fracture diagnosis techniques, which may lead to more precise and effective clinical decision-making in hospital settings.