Research on AI classification of thoracolumbar fractures based on deep convolutional neural network and transfer learning

HAO Yin; CHEN Xin; MO Yunhai; WU Luyuan; TONG Jingbo

doi:10.11959/j.issn.2096-6652.202426

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Research on AI classification of thoracolumbar fractures based on deep convolutional neural network and transfer learning

Papers and Reports | 更新时间：2024-11-04

- Research on AI classification of thoracolumbar fractures based on deep convolutional neural network and transfer learning
- Chinese Journal of Intelligent Science and Technology Vol. 6, Issue 3, Pages: 319-328(2024)
- 作者机构：
  
  1.四川省中西医结合医院放射科，四川成都 610041
  2.成都市第七人民医院放射科，四川成都 610044
  3.河南大学河南省人工智能理论及算法工程研究中心，河南开封 450001
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202426
  CLC： R687.3
- Received：14 May 2024，
  
  Revised：2024-07-23，
  
  Published：15 September 2024
- 稿件说明：
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郝引,陈馨,莫云海等.基于迁移学习和深度卷积神经网络的胸腰椎骨折AI分类研究[J].智能科学与技术学报,2024,06(03):319-328.

HAO Yin,CHEN Xin,MO Yunhai,et al.Research on AI classification of thoracolumbar fractures based on deep convolutional neural network and transfer learning[J].Chinese Journal of Intelligent Science and Technology,2024,06(03):319-328.
郝引,陈馨,莫云海等.基于迁移学习和深度卷积神经网络的胸腰椎骨折AI分类研究[J].智能科学与技术学报,2024,06(03):319-328. DOI： 10.11959/j.issn.2096-6652.202426.

HAO Yin,CHEN Xin,MO Yunhai,et al.Research on AI classification of thoracolumbar fractures based on deep convolutional neural network and transfer learning[J].Chinese Journal of Intelligent Science and Technology,2024,06(03):319-328. DOI： 10.11959/j.issn.2096-6652.202426.

摘要

传统的胸腰椎骨折影像辅助分类方法准确率低、泛化能力差，为此提出一种基于深度卷积神经网络方法辅助诊断的胸腰椎骨折AI分类方法。收集四川省中西医结合医院胸腰椎骨折患者CT影像图片共698张，建立数据集，其中单纯压缩性骨折（A类）279张，爆裂性骨折（B类）295张，正常（C类）124张。对传统卷积神经网络模型ResNet-50进行改进并融入迁移学习，对数据集进行训练，获得胸腰椎骨折AI分类模型。采用混淆矩阵评估预测模型分类性能，模型的训练集和验证集准确率分别为95.75%和96.36%，表明训练得到的智能分类模型具有较好的准确率和泛化能力。本文提出胸腰椎骨折影像辅助分类方法，可以提高人工诊断的效率和准确率。

Abstract

The traditional thoracolumbar fracture image-assisted classification method has low accuracy and poor generalization ability. Therefore

based on deep convolutional neural network

this paper proposes an AI classification method for thoracolumbar fracture for auxiliary diagnosis. Firstly

a total of 698 CT images of patients with lumbar spine fractures were collected from Sichuan Integrative Medicine Hospital

and a data set was established

including 279 compression fractures (category A)

295 burst fractures (category B)

and 124 normal (category C). Secondly

the convolutional neural network model ResNet-50 was modified and combined with transfer learning to train

verify and test the data set to obtain the AI classification model of thoracolumbar fracture. Then

the Confusion Matrix is used to evaluate the prediction model. The accuracy of the training set and the validation set of the model is 95.75% and 96.36%

respectively

indicating that the model obtained by training has good accuracy and generalization ability. This paper proposes an image-assisted classification method for thoracolumbar fracture

which can improve the efficiency and accuracy of manual diagnosis.

关键词

Keywords

references

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