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Related Experiment Video

Updated: Mar 12, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
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ResNet based backbone integrated YOLO framework for bone fracture detection.

Diptendu Bhattacharya1, Subhradip Das1, Tamal Biswas2

  • 1Department of Computer Science and Engineering, National Institute of Technology Agartala, Tripura, India.

Scientific Reports
|March 11, 2026
PubMed
Summary
This summary is machine-generated.

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Within the skeletal system, the structure of a bone, or osseous tissue, can be exemplified in a long bone, like the femur, where there are two types of osseous tissue: cortical and cancellous.
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A new hybrid ResYOLO11 model enhances bone fracture detection using artificial intelligence. This AI tool offers improved accuracy and speed for orthopedic specialists, aiding clinical decisions in medical diagnostics.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Computer-Aided Diagnostics

Background:

  • Accurate bone fracture detection is critical for effective patient treatment.
  • Artificial intelligence (AI) and machine learning (ML) are increasingly vital in medical diagnostics.
  • Existing models require optimization for precise fracture localization.

Purpose of the Study:

  • To introduce a novel hybrid ResYOLO11 architecture for enhanced bone fracture detection.
  • To combine the feature extraction strengths of ResNet50 with the detection efficiency of YOLO11.
  • To improve spatial feature representation, classification precision, and detection robustness in medical imaging.

Main Methods:

  • Developed a hybrid ResYOLO11 architecture integrating ResNet50 backbone with YOLO11 modules (C3K2, SPPF, C2PSA).
Keywords:
Bone X-rayBone abnormalityFeature extractionFracture detectionResNet50YOLO11.

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  • Trained and evaluated the model on the public GRAZPEDWRI-DX dataset.
  • Utilized precision, recall, mAP@50, and mAP@50-95 as key performance metrics.

    • Main Results:

    • ResYOLO11 achieved high precision scores (0.935-0.963) and mAP@50 scores (0.970-0.986) across various model sizes.
    • Demonstrated up to 4.2% higher mAP50 and 6.1% higher mAP50-95 compared to standard YOLO11 variants.
    • Achieved a 24% faster inference time for fracture detection.

    Conclusions:

    • The ResYOLO11 architecture offers a clinically robust and computationally efficient solution for AI-powered fracture diagnosis.
    • This AI model shows significant potential in assisting orthopedic specialists with accurate fracture identification.
    • The findings support the advancement of computer-aided diagnostics in clinical decision-making.