Synergisticintegrationofadvanced X-ray Techniques for Enhanced Fracture Detection

Author(s)	Anusha Adhikari, Dipannita Dey, Dipyaman Goswami, Shounak Chattopadhyay, Asima Adak, Anindya Sen
Country	India
Abstract	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
Keywords	X-ray Image, Preprocessing, Morphologicalgradient,Sobel,edge,Canny, edge detection, Fracture Detection, VGG-19, VGG-16, YOLOv8, Deep Learningmodels,Streamlit,App,Site.
Field	Engineering
Published In	Volume 6, Issue 4, July-August 2024
Published On	2024-08-10
DOI	https://doi.org/10.36948/ijfmr.2024.v06i04.25366

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