Multi-Region Bone Fracture Detection in X-Ray Images using Deep Learning
Main Article Content
Deekshith Bolla
Wisam Bukaita, PhD
http://orcid.org/0000-0001-6255-3848
http://orcid.org/0000-0001-6255-3848
Abstract
Accurate detection of bone fractures in radiographic images is a critical yet challenging task in medical diagnosis due to subtle visual cues and the need for region-specific interpretation. This study proposes a two-stage deep learning pipeline for multi-region fracture detection and classification in X-ray images. In the first stage, the Bone Fracture dataset is employed to localize anatomical regions with bounding box annotations and to detect the presence of fractures. In the second stage, the Bone Break Classification dataset is used to categorize fracture subtypes, including transverse, oblique, and avulsion. The pipeline integrates these heterogeneous datasets to first identify the anatomical region and fracture presence, and then classify the fracture type when detected. Models were developed using EfficientNetB0 backbones trained in TensorFlow/Keras, with preprocessing steps such as resizing, normalization, one-hot encoding, and augmentation. Experimental results showed a detection accuracy of more than 90% for region-level fracture identification and a classification accuracy of More than 90% for fracture subtypes, with F1-scores closely aligned. Grad-CAM visualizations further confirmed the interpretability of the learned features. These findings demonstrate that combining region-based and type-based datasets enhances robustness and clinical relevance, paving the way for reliable decision-support tools in medical imaging.
Keywords:
Bone fracture detection, Deep learning, X-ray image analysis, Multi-region classification, EfficientNetB0, Medical image interpretation
Article Details
How to Cite
BOLLA, Deekshith; BUKAITA, Wisam.
Multi-Region Bone Fracture Detection in X-Ray Images using Deep Learning.
Medical Research Archives, [S.l.], v. 13, n. 12, dec. 2025.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/7099>. Date accessed: 02 jan. 2026.
doi: https://doi.org/10.18103/mra.v13i12.7099.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
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2. Yadav, Rishabh, and Manish Sharma. 2022. “Hybrid Scale Fracture Network (SFNet): A Multi-Scale CNN with Edge-Aware Preprocessing for Bone Fracture Detection.” Biomedical Signal Processing and Control 77: 103757. https://doi.org/10.1016/j.bspc.2022.103757
3. Zou, Wenhao, Imran Arshad, and Tao Zhang. 2024. “Attention-Guided YOLOv7 for Robust Bone Fracture Detection in Radiographs.” Computers in Biology and Medicine 173: 108241. https://doi.org/10.1016/j.compbiomed.2024.108241
4. Singh, Rajdeep, Alireza Ardakani, and Nithin Varma. 2023. “Automated Scaphoid Fracture Detection Using Convolutional Neural Networks with Grad-CAM Visualization.” Diagnostics 13 (4): 742–54. https://doi.org/10.3390/diagnostics13040742
5. Breit, Michael F., and Anna Varga-Szemes. 2023. “Opportunistic Osteoporosis Screening Using Deep Learning on Chest CT: Correlation with DEXA.” European Journal of Radiology 162: 110850. https://doi.org/10.1016/j.ejrad.2023.110850
6. Hardalaç, Firat, and Mustafa Uysal. 2022. “Tiered Ensemble Framework for Wrist Fracture Detection Using Deep Learning.” Sensors 22 (19): 7341–56. https://doi.org/10.3390/s22197341
7. Meza, Luis, Srinivas Ganta, and Miguel Gonzalez Torres. 2024. “Hybrid-Attention YOLOv8 for Real-Time Bone Fracture Detection on Radiographs.” IEEE Access 12: 87645–87658. https://doi.org/10.1109/ACCESS.2024.3387645
8. Alex, Nina, and Putri Rosmasari. 2025. “Lightweight Transfer Learning for Bone Fracture Detection Using Fine-Tuned ResNet18.” Journal of Medical Imaging and Health Informatics 15 (3): 477–89. https://doi.org/10.1166/jmihi.2025.4051
9. Venkatesam, R., and N. Sreyas. 2025. “Transfer-Learned InceptionV3 for Long-Bone Fracture Detection in Computed Tomography.” Medical & Biological Engineering & Computing 63 (2): 259–71. https://doi.org/10.1007/s11517-025-02943-1
10. Parvin, Faria, and Mahmud Rahman. 2024. “Multi-Modal Human Bone Fracture Detection Using YOLOv8: A Unified Deep Learning Approach.” Diagnostics 14 (1): 54–68. https://doi.org/10.3390/diagnostics14010054
11. Bagaria, Swati, and Deepak Wadhwani. 2022. “Deep Convolutional Neural Networks for Bone Fracture Detection on X-Ray Images.” Biomedical Engineering Advances 3: 100047. https://doi.org/10.1016/j.bea.2022.100047
12. Chen, Xiaoming, and Liang Wu. 2023. “Explainable Deep Learning for Fracture Classification Using Grad-CAM Enhanced CNN Models.” Frontiers in Radiology 3: 112567. https://doi.org/10.3389/fradi.2023.112567
13. Rao, Pavan, and Priya Gupta. 2024. “Comparative Study of Transformer-Based Vision Models for Bone Fracture Detection.” Artificial Intelligence in Medicine 155: 102466. https://doi.org/10.1016/j.artmed.2024.102466
14. Patel, Karan, and Yue Zhang. 2025. “Federated Deep Learning Framework for Cross-Hospital Bone Fracture Diagnosis.” IEEE Journal of Biomedical and Health Informatics 29 (5): 908–21. https://doi.org/10.1109/JBHI.2025.3375284
15. Li, Zhen, and Qian Wang. 2023. “Vision Transformer with Local Feature Reinforcement for Skeletal X-Ray Fracture Detection.” Pattern Recognition Letters 174: 30–39. https://doi.org/10.1016/j.patrec.2023.02.012
16. Ahmed, Tariq, and Sana Ullah. 2024. “Dual-Stream CNN with Edge and Texture Features for Improved Bone Fracture Classification.” Computers in Biology and Medicine 174: 108403. https://doi.org/10.1016/j.compbiomed.2024.108403
17. Jeong, Hyun, and Sangwoo Park. 2023. “Semi-Supervised Learning for Pediatric Fracture Detection Using Limited Annotated Data.” Medical Image Analysis 89: 102982. https://doi.org/10.1016/j.media.2023.102982
18. Fernandez, Lucia, and Miguel Rojas. 2025. “Real-Time Bone Fracture Detection Using YOLOv9 and Self-Attention.” IEEE Access 13: 123944–123957. https://doi.org/10.1109/ACCESS.2025.3412901
19. Liu, Wei, Hao Zhang, Min Chen, and Qiang Li. 2024. “Hybrid Transformer Convolutional Neural Network-Based Radiomics Models for Osteoporosis Screening in Routine CT.” BMC Medical Imaging 24 (1): 45. https://doi.org/10.1186/s12880-024-01240-5
20. Kaggle. 2025. “Bone Fracture Detection: Computer Vision Project.” Accessed September 21, 2025. https://www.kaggle.com/datasets/pkdarabi/bone-fracture-detection-computer-vision-project
21. Kaggle. 2025. “Bone Break Classification: A Computer Vision DataSet.” Accessed August 25, 2025. https://www.kaggle.com/datasets/pkdarabi/bone-break-classification-image-dataset
22. Kutbi, Mohammed. 2024. “Artificial Intelligence-Based Applications for Bone Fracture Detection Using Medical Images: A Systematic Review.” Diagnostics 14 (17): 1879. https://doi.org/10.3390/diagnostics14171879
23. Su, Zhihao; Adam, Afzan; Nasrudin, Mohammad Faidzul; Ayob, Masri; Punganan, Gauthamen. 2023. “Skeletal Fracture Detection with Deep Learning: A Comprehensive Review.” Diagnostics 13 (20): 3245. https://doi.org/10.3390/diagnostics13203245
24. Wu, Jiangfen; Liu, Nijun; Li, Xianjun; Fan, Qianrui; Li, Zhihao; Shang, Jin; Wang, Fei; Chen, Bowei; Shen, Yuanwang; Cao, Pan; Liu, Zhe; Li, Miaoling; Qian, Jiayao; Yang, Jian; Sun, Qinli. 2023. “Convolutional Neural Network for Detecting Rib Fractures on Chest Radiographs: A Feasibility Study.” BMC Medical Imaging 23 (1): 18. https://doi.org/10.1186/s12880-023-00975-x
25. Cheng, Li-Wei; Chou, Hsin-Hung; Cai, Yu-Xuan; Huang, Kuo-Yuan; Hsieh, Chin-Chiang; Chu, Po-Lun; Cheng, I. Szu; Hsieh, Sun-Yuan. 2024. “Automated Detection of Vertebral Fractures from X-ray Images: A Novel Machine Learning Model and Survey of the Field.” Neurocomputing 566: 126946. https://doi.org/10.1016/j.neucom.2023.126946
26. Huang, Shu-Tien; Liu, Liong-Rung; Chiu, Hung-Wen; Huang, Ming-Yuan; Tsai, Ming-Feng. 2023. “Deep Convolutional Neural Network for Rib Fracture Recognition on Chest Radiographs.” Frontiers in Medicine. https://doi.org/10.3389/fmed.2023.1178798
27. Sumon, R. I., A. H. M. M. Ahammad, M. S. U. Khan, and S. M. A. Hashem. 2025. “Automatic Fracture Detection Convolutional Neural Network with Multiple Attention Blocks Using Multi-Region X-Ray Data.” Life 15 (7): 1135. https://doi.org/10.3390/life15071135
28. Tahir, Ayesha; Saadia, Ayesha; Khan, Khurram; Qahmash, Ayman; Akram, Raja Naeem. 2024. “Enhancing Diagnosis: Ensemble Deep-Learning Model for Fracture Detection Using X-Ray Images.” Clinical Radiology 79 (11): e1394–e1402. https://doi.org/10.1016/j.crad.2024.08.006
29. Ahmed, K. Dlshad; Hawezi, F.; et al. 2023. “Detection of Bone Fracture Based on Machine Learning Techniques.” Measurement: Sensors 27: 100723. https://doi.org/10.1016/j.measen.2023.100723
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