Advanced feature fusion of radiomics and deep learning for accurate detection of wrist fractures on X-ray images.
Authors
Affiliations (13)
Affiliations (13)
- Faculty of Pharmacy, Middle East University, Amman, 11831, Jordan.
- College of Pharmacy, Alnoor University, Mosul, Iraq.
- Ahl Al Bayt University, Kerbala, Iraq.
- Department of Computer Engineering, Faculty of Engineering & Technology, Marwadi University Research Center, Marwadi University, Rajkot, 360003, Gujarat, India.
- Department of Chemistry and Biochemistry, School of Sciences, JAIN (Deemed to Be University), Bangalore, Karnataka, India.
- Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, India.
- Department of Chemistry, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India.
- Department of Public Health and Healthcare Management, Rector, Samarkand State Medical University, 18, Amir Temur Street, Samarkand, Uzbekistan.
- College of Nursing, National University of Science and Technology, Dhi Qar, Iraq.
- Pharmacy College, Al-Farahidi University, Baghdad, Iraq.
- Department of Pharmacy, Al-Zahrawi University College, Karbala, Iraq.
- Gilgamesh Ahliya University, Baghdad, Iraq.
- Department of Medical Physics and Radiology, Faculty of Paramedical Sciences, Kashan University of Medical Sciences, Kashan, Iran. [email protected].
Abstract
The aim of this study was to develop a hybrid diagnostic framework integrating radiomic and deep features for accurate and reproducible detection and classification of wrist fractures using X-ray images. A total of 3,537 X-ray images, including 1,871 fracture and 1,666 non-fracture cases, were collected from three healthcare centers. Radiomic features were extracted using the PyRadiomics library, and deep features were derived from the bottleneck layer of an autoencoder. Both feature modalities underwent reliability assessment via Intraclass Correlation Coefficient (ICC) and cosine similarity. Feature selection methods, including ANOVA, Mutual Information (MI), Principal Component Analysis (PCA), and Recursive Feature Elimination (RFE), were applied to optimize the feature set. Classifiers such as XGBoost, CatBoost, Random Forest, and a Voting Classifier were used to evaluate diagnostic performance. The dataset was divided into training (70%) and testing (30%) sets, and metrics such as accuracy, sensitivity, and AUC-ROC were used for evaluation. The combined radiomic and deep feature approach consistently outperformed standalone methods. The Voting Classifier paired with MI achieved the highest performance, with a test accuracy of 95%, sensitivity of 94%, and AUC-ROC of 96%. The end-to-end model achieved competitive results with an accuracy of 93% and AUC-ROC of 94%. SHAP analysis and t-SNE visualizations confirmed the interpretability and robustness of the selected features. This hybrid framework demonstrates the potential for integrating radiomic and deep features to enhance diagnostic performance for wrist and forearm fractures, providing a reliable and interpretable solution suitable for clinical applications.