A Hybrid Quantum Classical Pipeline for X Ray Based Fracture Diagnosis

TL;DR

This paper introduces a hybrid quantum-classical pipeline that enhances X-ray fracture diagnosis by combining PCA and quantum encoding, achieving high accuracy with reduced feature extraction time.

Contribution

It presents a novel hybrid quantum classical pipeline that improves fracture detection accuracy and efficiency over traditional methods.

Findings

Achieved 99% classification accuracy on X-ray dataset.

Reduced feature extraction time by 82%.

Comparable performance to state-of-the-art transfer learning models.

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%.