Synergisticintegrationofadvanced X-ray Techniques for Enhanced Fracture Detection

Deep learning models including VGG-16, VGG-19, YOLO v8, and a customized model are employed for fracture detection on the processed images and further utilizingStreamlit’suser-friendlyinterface,users can easily input X-ray images for pre-processing and fracture detection.Our study addresses the imperative role of X-ray imaging in medical diagnosis, emphasizing the significance of pre--processing techniques to enhanceimageclarity.Thisworkdeveloped an interactive web application to access X-raybasedmedicalimagesfordiagnosticpurpose using Streamlit, a Python library.Incorporating a bounding box feature, our system enables fracture localization using the pretrained VGG-19 model.Additionally, users can download the processed images, facilitatingefficientfractureidentification and seamless pre-processing.This integrated approach enhances the diagnostic capabilityofX-rayimaging,enablingaccurate fracture localization and fostering improved patient care through the amalgamationofstate-of-the-artdeeplearningmodels andadvancedimageprocessingtechniques