A Hybrid Quantum Classical Pipeline for X Ray Based Fracture Diagnosis

-- Bone fractures are a leading cause of morbidity and disability worldwide, imposing significant clinical and economic burdens on healthcare systems. Traditional X - ray inter pretation 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 hyb rid 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%. I. INTRODUCTION one fractures present a major challenge in orthopedic and trauma care, where accurate and timely diagnosis is critical for effective trea tment and patient recovery. These may result from trauma, accidents, or conditions like osteoporosis, and if fractures are misdiagnosed or undiagnosed, patients may suffer complications such as improper heali ng or long - term disability [1]. Globally, the fractures contribute substantially to morbidity, disability, and healthcare costs [1 ], [ 2]. X - ray imaging remains the most common diagnostic tool due to its accessibility and non - invasive nature.