A Dual-Model Interpretable Pipeline for Multi-Class Bone Fracture Recognition and Clinical Decision Support

Abstract

Bone fracture diagnosis from X-ray images is a critical task in clinical settings, traditionally performed by radiologists through manual inspection. However, such approaches are time-consuming, subject to human error, and may not be scalable for large patient volumes. A current trend across several industries involves utilizing computer-based technologies to identify faults. To meet the demands of immediate detection and high precision, a highly responsive system should leverage modern approaches and make full use of available resources. While various methods exist for detecting bone fractures in the modern world, such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) scans, and Bone scans, these approaches tend to be more expensive, uncomfortable for patients, and less effective at detecting subtle fractures that, if left untreated, could lead to significant challenges. The system is founded on a transfer learning strategy, utilizing the Data-efficient Image Transformer (DieT) as a fixed, high-performance feature extractor to convert raw X-ray data into robust, condensed numerical embeddings. These sophisticated feature vectors are subsequently used to train the central classification engine: the novel and highly constrained Fast Interpretable Greedy-tree Sums (FIGS) classifier. The superior performance of FIGS is established by benchmarking its accuracy, precision, and recall against traditional linear classifiers like Ridge Classifier (RC) and Passive Aggressive Classifier (PAC). Crucially, the system is augmented by an eXplainable AI (XAI) module, which acts as a robust prescreener to validate the image type and provide a detailed, human-readable analysis including bone identification, fracture type, and severity before the FIGS model delivers its final classification.