Usage of intelligent medical aided diagnosis system under the deep convolutional neural network in lumbar disc herniation: Influence Statistics

Usage of intelligent medical aided diagnosis system under the deep convolutional neural network in lumbar disc herniation

Abstract

. In order to improve the diagnosis efficiency of lumbar disc herniation (LDH) and reduce the impact of manual intervention in the traditional computer-aided diagnosis (CAD) system, the magnetic resonance imaging (MRI) images of LDH patients were selected as the research objects in this study. Firstly, the conditional deep convolutional generative adversarial network (CDCGAN) was constructed, and the influence of the improved T-ReLu activation function on the classification effect of the model was analyzed comparatively. Secondly, the model was applied to classification of MRI images of LDH patients to analyzed comparatively the effects of different parameters on the classification accuracy. Then, an aided diagnosis system of LDH covering the MRI feature extraction, 3D modeling, and CDCGAN model classification was built, and the model was tested with the real data. Finally, the Spearman correlation was adopted to analyze the correlation between the MRI quantitative indexes based on the aided diagnosis system of LDH and the course of LDH. It was found that the classification accuracy of the CDCGAN model with improved activation function on the Cifar-100 data set was 96.07%, and the classification accuracy of MRI images of LDH patients was 94.41% when the parameter N was 85. After the cross-validation, it was found that the diagnostic accuracy of the aided diagnosis system of LDH constructed in this study was 94.15% on LDH diseases. In addition, it was found that the Kyphotic angle of the herniated dise value and the relative signal intensity value in the MRI quantitative indicators showed a very significant negative correlation with the prevalence of LDH (P < 0.01), while the index of disc herniation, nucleus protrusion rate, ratio of horizontal deviation angle, and the ratio between the protruded part and the dural sac showed extremely positive correlations with the course of LDH (P < 0.01). It suggested that applying the CDCGAN model to the aided diagnosis system of LDH based on the MRI quantitative indicators could improve the accuracy of diagnosis of LDH.