A Hybrid Quantum Classical Pipeline for X Ray Based Fracture Diagnosis
Journal:
arXiv
Published Date:
May 19, 2025
Abstract
Bone fractures are a leading cause of morbidity and disability worldwide,
imposing significant clinical and economic burdens on healthcare systems.
Traditional X ray interpretation is time consuming and error prone, while
existing machine learning and deep learning solutions often demand extensive
feature engineering, large, annotated datasets, and high computational
resources. To address these challenges, a distributed hybrid quantum classical
pipeline is proposed that first applies Principal Component Analysis (PCA) for
dimensionality reduction and then leverages a 4 qubit quantum amplitude
encoding circuit for feature enrichment. By fusing eight PCA derived features
with eight quantum enhanced features into a 16 dimensional vector and then
classifying with different machine learning models achieving 99% accuracy using
a public multi region X ray dataset on par with state of the art transfer
learning models while reducing feature extraction time by 82%.
