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Background <p>To construct an improved deep learning model that provides a more accurate and cost-effective solution for diagnosing abnormalities in the cervical physiological curvature.</p> Methods <p>This study included 240 patients who were diagnosed with cervical spondyosis at our hospital from 2020–2024. Their X-ray images were collected and classified into three categories: normal cervical curvature, straightened cervical curvature, and reversed cervical curvature. The original image data were processed using the YOLO-V3 model for object detection, followed by data augmentation to generate an experimental dataset. On the basis of the ResNet-34 architecture, this study introduces a learnable weight matrix integrated with convolutional operations within the residual blocks to enhance the model’s nonlinear representation capability, thereby leading to the construction of the proposed S-ResNet-34 model.</p> Results <p>Validation based on the test set indicated that the S-ResNet-34model achieved an accuracy (ACC) of 90.94%, an F1 score of 85.27%, and a recall rate of 85.36%. Compared with that of other models (CNN, SVM, RNN, DenseNet, and ResNet-152), the S-ResNet-34 model demonstrated superior performance in distinguishing cervical curvature abnormalities in small-sample X-ray image data.</p> Conclusion <p>This study introduced a deep learned-based model, S-ResNet-34, which is an innovative approach to auxiliary diagnosis for small-sample X-ray image analysis. The findings offer a new choice for patients by simplifying the diagnostic process while maintaining diagnostic accuracy.</p>

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S-ResNet-34: small sample-ResNet-34 for predicting cervical degeneration in x-ray image data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月1日

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  • Zihan Wei,
  • Han Wu,
  • Yifei Xu,
  • Tao Zhang,
  • Tongwei Chu,
  • Liang Chen,
  • Sizhen Yang

摘要

Background

To construct an improved deep learning model that provides a more accurate and cost-effective solution for diagnosing abnormalities in the cervical physiological curvature.

Methods

This study included 240 patients who were diagnosed with cervical spondyosis at our hospital from 2020–2024. Their X-ray images were collected and classified into three categories: normal cervical curvature, straightened cervical curvature, and reversed cervical curvature. The original image data were processed using the YOLO-V3 model for object detection, followed by data augmentation to generate an experimental dataset. On the basis of the ResNet-34 architecture, this study introduces a learnable weight matrix integrated with convolutional operations within the residual blocks to enhance the model’s nonlinear representation capability, thereby leading to the construction of the proposed S-ResNet-34 model.

Results

Validation based on the test set indicated that the S-ResNet-34model achieved an accuracy (ACC) of 90.94%, an F1 score of 85.27%, and a recall rate of 85.36%. Compared with that of other models (CNN, SVM, RNN, DenseNet, and ResNet-152), the S-ResNet-34 model demonstrated superior performance in distinguishing cervical curvature abnormalities in small-sample X-ray image data.

Conclusion

This study introduced a deep learned-based model, S-ResNet-34, which is an innovative approach to auxiliary diagnosis for small-sample X-ray image analysis. The findings offer a new choice for patients by simplifying the diagnostic process while maintaining diagnostic accuracy.