Abstract:
Since X-ray image interpretation being subjective, bone fractures present
substantial obstacles for medical diagnosis and can occasionally result in inaccurate
diagnoses and treatment delays. Our proposal involves using convolutional neural
networks like ResNet50 in a machine learning approach to tackle this problem. Through
the development of a reliable system for automated fracture identification and
classification, our method seeks to increase diagnostic accuracy and lessen reliance on
human diagnosis. Through the use of a dataset from the MURA collection to train our
deep learning model, we have created an effective tool that can accurately diagnose a
variety of bone fracture forms. Fast uploading of X-ray pictures is made possible by the
user-friendly interface, which enables quick predictions on the existence and
categorization of fractures. Additionally, our approach improves clinical decisionmaking
by offering customized therapy suggestions based on the examination of these
photos. Our model has performed exceptionally well in evaluations, with 95% accuracy
rate in fracture classification and identification. These results demonstrate the efficacy
of our approach in improving clinical diagnostic performance and patient outcomes.
Our ultimate objective is to optimize the diagnostic procedure, relieving the timeconsuming
workload for healthcare providers and guaranteeing prompt and precise
patient care. In final analysis, the urgent demand for trustworthy automated systems for
bone fracture detection is addressed by our research. We want to transform medical
imaging and open the door to better patient outcomes and healthcare delivery by
utilizing AI and machine learning.
