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

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

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

Published at

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

Halima Khatun
Halima Khatun

Halima Khatun

  • Research Assistant, CCDS

Bachelors

Independent University, Bangladesh

Published at

Scientific Reports

Riyadul Islam
Riyadul Islam

Riyadul Islam

  • Intern, CCDS

Bachelors

Daffodil International University

Published at

Scientific Reports

MD Akil Raihan Iftee
Md Akil Raihan Iftee

MD Akil Raihan Iftee

  • Research Assistant, CCDS

Published at

IJCNN, ICIP

Research Interests

Multimodal Learning, Computer Vision, Trustworthy ML

Ashraful Islam, PhD
Ashraful Islam

Ashraful Islam, PhD

  • Center Director, CCDS
  • Director, HCI Wing

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh (IUB)

Published at

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

Research Interests

Human-Computer Interaction, AI for Social Good, AI for Public Health, AI for Impact

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