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Classification of Cervical Spine Fracture and Dislocation Using Refined Pre-Trained Deep Model and Saliency Map.

Soaad M Naguib1, Hanaa M Hamza2, Khalid M Hosny2

  • 1Department of Information Systems, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt.

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Summary
This summary is machine-generated.

This study introduces a deep learning system using AlexNet and GoogleNet to accurately diagnose cervical spine (CS) fractures and dislocations from X-rays, aiding emergency medical decisions.

Keywords:
X-raycervical spine dislocationcervical spine fracturescomputer aided-diagnosis systemdeep learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Orthopedics

Background:

  • Cervical spine (CS) injuries, including fractures and dislocations, are critical emergencies requiring immediate diagnosis to prevent severe outcomes like paralysis.
  • Timely and accurate diagnosis of CS injuries is essential for effective emergency medical intervention.

Purpose of the Study:

  • To develop and evaluate a computer-aided diagnosis (CAD) system utilizing deep learning for the classification of cervical spine fractures and dislocations.
  • To assist physicians in the rapid and accurate diagnosis of CS injuries, particularly within emergency settings.

Main Methods:

  • A deep learning model, incorporating AlexNet and GoogleNet architectures, was developed for image classification.
  • The model was trained on a dataset of 2009 cervical spine X-ray images, categorized into dislocations, fractures, and normal cases.
  • Saliency maps were employed to analyze the spatial localization of diagnostic features within the images.

Main Results:

  • The proposed deep learning system achieved high diagnostic performance, with accuracy reaching 99.56%.
  • Exceptional sensitivity (99.33%), specificity (99.67%), and precision (99.33%) were recorded, demonstrating the system's reliability.
  • Saliency map analysis confirmed the model's focus on relevant image regions for classification.

Conclusions:

  • The developed deep learning-based CAD system demonstrates high accuracy and reliability in classifying cervical spine fractures and dislocations from X-ray images.
  • This technology has the potential to significantly enhance diagnostic capabilities in emergency services, supporting clinical decision-making.
  • The system's integration with imaging devices could enable real-time clinical decisions for CS injuries.