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For many years, cervical spine fractures and dislocations are major cause of paralysis and death in some cases. This makes it important for the proper diagnosis and treatment of these injuries to reduce fatal injuries. There are many methods to do this, and in this paper we present a computer-aided cervical spine injury diagnosis model that uses deep learning approaches like AlexNet and GoogleNet. The proposed model can be used by doctors for faster identification of cervical injuries. To train our model, we have used 772 CS fractures and 707 normal images. The model came up with the accuracy of 99.67%, which is higher than accuracy of radiologists; we have also used saliency maps to check degree of instance for a given class. This paper has both clinical and research-based applications.

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Classification of Cervical Spine Fracture Using Deep Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Arunesh Tiwari,
  • Swapnil Singh,
  • Adarsh Pandey,
  • Brijendra Pratap Singh,
  • Dinesh Kumar,
  • Dharmendra Kumar

摘要

For many years, cervical spine fractures and dislocations are major cause of paralysis and death in some cases. This makes it important for the proper diagnosis and treatment of these injuries to reduce fatal injuries. There are many methods to do this, and in this paper we present a computer-aided cervical spine injury diagnosis model that uses deep learning approaches like AlexNet and GoogleNet. The proposed model can be used by doctors for faster identification of cervical injuries. To train our model, we have used 772 CS fractures and 707 normal images. The model came up with the accuracy of 99.67%, which is higher than accuracy of radiologists; we have also used saliency maps to check degree of instance for a given class. This paper has both clinical and research-based applications.