[1]HONG Yanfei,WEI Benzheng,LIU Chuan,et al.Deep learning based automatic multi-classification algorithm for intervertebral foraminal stenosis[J].CAAI Transactions on Intelligent Systems,2019,14(4):708-715.[doi:10.11992/tis.201806015]
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Deep learning based automatic multi-classification algorithm for intervertebral foraminal stenosis

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 4 Page number: 708-715 Column: 学术论文—机器学习 Public date: 2019-07-02
Title:
Deep learning based automatic multi-classification algorithm for intervertebral foraminal stenosis
Author(s):
HONG Yanfei1; 2;  WEI Benzheng1; 2;  LIU Chuan2;  HAN Zhongyi1; 2;  LI Tianyang1; 2
1. College of Science and Technology, Shandong University of Traditional Chinese Medicine, Ji’nan 250355, China;
2. Computational Medicine Lab, Shandong University of Traditional Chinese Medicine, Ji’nan 250355, China
Keywords:
intervertebral foraminal stenosis; automatic grade; machine learning; deep learning; feature extraction; supervised training; transfer learning; over fitting
CLC:
TP311
DOI:
10.11992/tis.201806015
Abstract:
Preoperative qualitative diagnosis of intervertebral foraminal stenosis is essential for the formulation of clinician treatment strategies and patients’ health recovery. However, there are still many clinical challenges in this aspect and a lack of relevant research and proven methods to assist clinicians in diagnosis. Therefore, a deep learning-based automatic classification algorithm is proposed in this study to improve the diagnosis accuracy and the efficiency. First, we extracted the spinal foramen images from the sagittal spine MRI image, and then these images were preprocessed. Second, a supervised deep convolutional neural network model was designed to achieve automatic multi-classification for the datasets of the intervertebral foraminal stenosis. Finally, we used the transfer learning to optimize the overfitting problem of the deep learning algorithm in the small sample dataset. The experimental results show that the classification accuracy of this algorithm on the dataset of spinal foramen was 87.5%, and it has good robustness and generalization performance.
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