A Hybrid Quantum Classical Pipeline for X Ray Based Fracture Diagnosis
This addresses the challenge of time-consuming and error-prone X-ray interpretation for fracture diagnosis, though it appears incremental as it builds on existing quantum and classical methods.
The paper tackles the problem of automated bone fracture diagnosis from X-rays by proposing a hybrid quantum-classical pipeline that combines PCA dimensionality reduction with quantum amplitude encoding for feature enrichment, achieving 99% accuracy on a public dataset while reducing feature extraction time by 82%.
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%.