Author Login IJIRT Partner Network (IPN) Login

Bone fracture detection and classification using deep learning

  • Unique Paper ID: 175166
  • Volume: 11
  • Issue: 11
  • PageNo: 1914-1923
  • Abstract:
  • Timely and accurate classification of bone fractures is essential for effective orthopedic diagnosis and treatment planning. This study proposes a deep learning-based multiclass classification framework for automated bone fracture detection using radiographic images, without the need for segmentation or localization techniques. We evaluate and compare the performance of three state-of-the-art convolutional neural network architectures—DenseNet-121, EfficientNet-B0, and ResNet-50—for classifying ten different fracture types, including comminuted, oblique, spiral, and pathological fractures. The models are trained on a labeled dataset with class balancing and data augmentation strategies to improve generalization. Experimental results demonstrate that DenseNet-121 achieves the highest classification accuracy of 98.94%, followed by EfficientNet-B0 with 98.76%, and ResNet-50 with 98.50%. Evaluation metrics such as precision, recall, and F1-score further confirm the robustness and reliability of the proposed approach. The findings highlight the effectiveness of deep learning models in automated fracture classification, offering a scalable solution to support clinical decision-making and reduce diagnostic workload.
Download article email to a friend

Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 11
  • PageNo: 1914-1923

Bone fracture detection and classification using deep learning

Related Articles

Impact Factor
8.01 (Year 2024)
UGC Approved
Journal no 47859

Join Our IPN

IJIRT Partner Network

Submit your research paper and those of your network (friends, colleagues, or peers) through your IPN account, and receive 800 INR for each paper that gets published.

Join Now

Latest Publication

Archive

View More

Recent Conferences

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024 Last Date: 15th March 2024

Submit inquiry