Conference Paper2025Cited by 1

Patch-Based Deep Ensemble Learning for Enhancing Pelvic Fracture Detection

Siam Tahsin Bhuiyan, Rashedur Rahman, Saadia Binte Alam, Halima Khatun, Riyadul Islam, Sefatul Wasi, Md Akil Raihan Iftee, Ashraful Islam

2025 28th International Conference on Computer and Information Technology (ICCIT)

IEEE, pp. 3022-3027

  • Good Health and Well-being — Relevance: 95%
DOI: 10.1109/iccit68739.2025.11491331URL

Abstract

Accurate detection of pelvic fractures is critical in trauma care but remains challenging due to the pelvis's complex anatomy and the subtle nature of some fractures. While deep learning shows promise, conventional CNNs and ensembles struggle with localized fractures due to their reliance on global features. In this study, we introduce Patch Ensemble, a novel framework that enhances fracture detection by dividing X-rays into left and right patches, extracting localized features with separate CNN backbones, and combining them for classification. We evaluate Patch Ensemble by comparing against Conventional CNN and Conventional Ensemble methods using the PXR150 dataset. The proposed method outperforms both baselines, achieving an average AUROC of 0.87, compared to 0.77 for Conventional CNN and 0.79 for Conventional Ensemble. Patch Ensemble also achieves superior recall (0.87) and specificity (0.82). Our results demonstrate that Patch Ensemble offers a robust and accurate deep learning solution for pelvic fracture detection, providing a promising tool to assist radiologists in trauma care.

Keywords

  • ensemble deep learning
  • trauma care
  • X-ray images

CCDS Authors

Siam Tahsin Bhuiyan
Siam Tahsin Bhuiyan

Siam Tahsin Bhuiyan

  • Intern, CCDS

  • Bachelors

    Independent University, Bangladesh (IUB)

Published at

Scientific Reports

Md Rashedur Rahman, D. Eng.
Md. Rashedur Rahman, D. Eng.

Md Rashedur Rahman, D. Eng.

  • Co-Director, MIRA & NEST Wings

  • Assistant Professor

    Department of Computer Science and Engineering

    Independent University, Bangladesh (IUB)

Published at

CHI, HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

Saadia Binte Alam, PhD
Saadia Binte Alam, PhD

Saadia Binte Alam, PhD

  • Director, MIRA & WiSE Wings

  • Associate Professor

    Department of Computer Science and Engineering

    Independent University, Bangladesh (IUB)

Published at

HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

Halima Khatun
Halima Khatun

Halima Khatun

  • Research Assistant, CCDS

  • Bachelors

    Independent University, Bangladesh (IUB)

Published at

Scientific Reports

Riyadul Islam
Riyadul Islam

Riyadul Islam

  • Intern, CCDS

  • Bachelors

    Daffodil International University, Bangladesh

Published at

Scientific Reports

MD Md Akil Raihan Iftee
Md Akil Raihan Iftee

MD Md Akil Raihan Iftee

  • Alumni, CCDS

  • PhD Student

    Rochester Institute of Technology, USA

  • BSc

    Khulna University of Engineering & Technology, Bangladesh

Published at

IJCNN, ICIP

Ashraful Islam, PhD
Ashraful Islam

Ashraful Islam, PhD

  • Center Director, CCDS
  • Director, HCI Wing

Published at

CHI, UIST, UbiComp, IJCNN, ICIP, HCII, ICDMW, BLP, Pattern Recognition, IEEE Internet of Things Journal, JMIR, JMIR Human Factors, JMIR Formative Research, IEEE Access, Smart Agricultural Technology, Diabetes Research and Clinical Practice, PLOS ONE

References

  1. 1.Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, Li Fei-Fei. (2009). ImageNet: A large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248–255[10.1109/cvpr.2009.5206848]
  2. 2.Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. , 4510–4520[10.1109/cvpr.2018.00474]
  3. 3.Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, Dhruv Batra. (2019). Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. International Journal of Computer Vision, 128(2), 336–359[10.1007/s11263-019-01228-7]
  4. 4.Charles M. Court-Brown, Ben Caesar. (2006). Epidemiology of adult fractures: A review. Injury, 37(8), 691–697[10.1016/j.injury.2006.04.130]
  5. 5.Guansong Pang, Chunhua Shen, Longbing Cao, Anton Van Den Hengel. (2021). Deep Learning for Anomaly Detection. ACM Computing Surveys, 54(2), 1–38[10.1145/3439950]
  6. 6.Seok Won Chung, Seung Seog Han, Ji Whan Lee, Kyung-Soo Oh, Na Ra Kim, Jong Pil Yoon, Joon Yub Kim, Sung Hoon Moon, Jieun Kwon, Hyo-Jin Lee, Young-Min Noh, Youngjun Kim. (2018). Automated detection and classification of the proximal humerus fracture by using deep learning algorithm. Acta Orthopaedica, 89(4), 468–473[10.1080/17453674.2018.1453714]
  7. 7.Cheng Ju, Aurélien Bibaut, Mark van der Laan. (2018). The relative performance of ensemble methods with deep convolutional neural networks for image classification. Journal of Applied Statistics, 45(15), 2800–2818[10.1080/02664763.2018.1441383]
  8. 8.Zsolt Balogh, Kate L. King, Peter Mackay, Debra McDougall, Stuart Mackenzie, Julie A. Evans, Timothy Lyons, Stephen A. Deane. (2007). The Epidemiology of Pelvic Ring Fractures: A Population-Based Study. The Journal of Trauma: Injury, Infection, and Critical Care, 63(5), 1066–1073[10.1097/ta.0b013e3181589fa4]
  9. 9.Tyler J. Bradshaw, Zachary Huemann, Junjie Hu, Arman Rahmim. (2023). A Guide to Cross-Validation for Artificial Intelligence in Medical Imaging. Radiology Artificial Intelligence, 5(4), e220232[10.1148/ryai.220232]
  10. 10.Dominik Muller, Inaki Soto-Rey, Frank Kramer. (2022). An Analysis on Ensemble Learning Optimized Medical Image Classification With Deep Convolutional Neural Networks. IEEE Access, 10, 66467–66480[10.1109/access.2022.3182399]
  11. 11.Matthew P. Sullivan, Keith D. Baldwin, Derek J. Donegan, Samir Mehta, Jaimo Ahn. (2014). Geriatric Fractures About the Hip: Divergent Patterns in the Proximal Femur, Acetabulum, and Pelvis. Orthopedics, 37(3), 151–157[10.3928/01477447-20140225-50]
  12. 12.Chi-Tung Cheng, Yirui Wang, Huan-Wu Chen, Po-Meng Hsiao, Chun-Nan Yeh, Chi-Hsun Hsieh, Shun Miao, Jing Xiao, Chien-Hung Liao, Le Lu. (2021). A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs. Nature Communications, 12(1), 1066[10.1038/s41467-021-21311-3]
  13. 13.Husham Abdelrahman, Ayman El-Menyar, Holger Keil, Abduljabbar Alhammoud, Syed Imran Ghouri, Elhadi Babikir, Mohammad Asim, Matthias Muenzberg, Hassan Al-Thani. (2020). Patterns, management, and outcomes of traumatic pelvic fracture: insights from a multicenter study. Journal of Orthopaedic Surgery and Research, 15(1), 249[10.1186/s13018-020-01772-w]
  14. 14.Changhwan Lee, Jongseong Jang, Seunghun Lee, Young Soo Kim, Young Soo Kim, Hang Joon Jo, Yeesuk Kim, Yeesuk Kim. (2020). Classification of femur fracture in pelvic X-ray images using meta-learned deep neural network. Scientific Reports, 10(1), 13694[10.1038/s41598-020-70660-4]
  15. 15.Norio Nakata, Tsuyoshi Siina. (2023). Ensemble Learning of Multiple Models Using Deep Learning for Multiclass Classification of Ultrasound Images of Hepatic Masses. Bioengineering, 10(1), 69[10.3390/bioengineering10010069]
  16. 16.Timothy J. O’Connor, Peter A. Cole. (2014). Pelvic Insufficiency Fractures. Geriatric Orthopaedic Surgery & Rehabilitation, 5(4), 178–190[10.1177/2151458514548895]
  17. 17.Rashedur Rahman, Naomi Yagi, Keigo Hayashi, Akihiro Maruo, Hirotsugu Muratsu, Syoji Kobashi. (2024). Enhancing fracture diagnosis in pelvic X-rays by deep convolutional neural network with synthesized images from 3D-CT. Scientific Reports, 14(1), 8004[10.1038/s41598-024-58810-4]
  18. 18.Francesca Rosa, Duccio Buccicardi, Adolfo Romano, Fabio Borda, Maria Chiara D’Auria, Alessandro Gastaldo. (2023). Artificial intelligence and pelvic fracture diagnosis on X-rays: a preliminary study on performance, workflow integration and radiologists' feedback assessment in a spoke emergency hospital. European Journal of Radiology Open, 11, 100504[10.1016/j.ejro.2023.100504]
  19. 19.Sefatul Wasi, Saadia Binte Alam, Rashedur Rahman, M Ashraful Amin, Syoji Kobashi. (2023). Kidney Tumor Recognition from Abdominal CT Images using Transfer Learning. , 54–58[10.1109/ismvl57333.2023.00021]
  20. 20.Mengxia Wang, Bo Zhuang, Shian Yu, Gang Li. (2024). Ensemble learning enhances the precision of preliminary detection of primary hepatocellular carcinoma based on serological and demographic indices. Frontiers in Oncology, 14, 1397505[10.3389/fonc.2024.1397505]
  21. 21.Yüksel Maraş, Ahmet Kor, Semra Duran, Kevser Orhan, Ebru Atalar, Emine Sena Sözen, Kemal Üreten, Hadi Hakan Maraş. (2024). USING ENSEMBLE LEARNING AND FEATURE SELECTION IN THE DIAGNOSIS OF LOW BACK PAIN. Rheumatology Quarterly, 2(3), 121–129[10.4274/qrheumatol.galenos.2024.85057]

Cited By

  1. 1.Sayeda Subha Ashrafi, Tushar Ahmed Shishir. (2026). Deep Learning-Based Automated Breast Cancer Detection from Histopathology Images: Benchmarking CNN and Transformer Architectures. , 1–6[10.1109/peccii70991.2026.11662018]
← All publications