智能科学与技术学报2024，Vol.6Issue(3)：319-328,10.DOI:10.11959/j.issn.2096-6652.202426
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
Research on AI classification of thoracolumbar fractures based on deep convolutional neural network and transfer learning
郝引 1陈馨 1莫云海 2吴禄源 3仝敬博3
作者信息
- 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
- 折叠
摘要
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
胸腰椎骨折/深度卷积神经网络/AI分类方法/泛化能力Key words
thoracolumbar fracture/deep convolutional neural network/AI classification method/generalization ability分类
医药卫生引用本文复制引用
郝引,陈馨,莫云海,吴禄源,仝敬博..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,2024,6(3):319-328,10.
