Context-Aware Level-Wise Feature Fusion Network with Anomaly Focus for Precise Classification of Incomplete Atypical Femoral Fractures in X-Ray Images

Abstract

Incomplete Atypical Femoral Fracture (IAFF) is a precursor to Atypical Femoral Fracture (AFF). If untreated, it progresses to a complete fracture, increasing mortality risk. However, due to their small and ambiguous features, IAFFs are often misdiagnosed even by specialists. In this paper, we propose a novel approach for accurately classifying IAFFs in X-ray images across various radiographic views. We design a Dual Context-aware Complementary Extractor (DCCE) to capture both the overall femur characteristics and IAFF details with the surrounding context, minimizing information loss. We also develop a Level-wise Perspective-preserving Fusion Network (LPFN) that preserves the perspective of features while integrating them at different levels to enhance model representation and sensitivity by learning complex correlations and features that are difficult to obtain independently. Additionally, we incorporate the Spatial Anomaly Focus Enhancer (SAFE) to emphasize anomalous regions, preventing the model bias toward normal regions, and reducing False Negatives and missed IAFFs. Experimental results show significant improvements across all evaluation metrics, demonstrating high reliability in terms of accuracy (0.931), F1-score (0.9456), and AUROC (0.9692), proving the model’s potential for application in real medical settings.

Keywords

• Incomplete Atypical Femoral Fracture
• Atypical Femoral Fracture
• X-ray
• feature fusion
• anomaly focus
• tiny lesion