Semi-Supervised Support Vector Machine for Digital Twins Based Brain Image Fusion.
Semi-Supervised Support Vector Machine for Digital Twins Based Brain Image Fusion.
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DOI：
10.3389/fnins.2021.705323
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发表时间：
2021
影响因子：
4.3
通讯作者：
Lv Z
中科院分区：
文献类型：
--
作者：
Wan Z;Dong Y;Yu Z;Lv H;Lv Z
探讨半监督支持向量机(S3VM)在脑图像融合数字双生子(DTES)中的特征识别、诊断和预测性能。针对脑图像中大量未标记数据同时使用未标记数据和已标记数据的特点，提出了半监督支持向量机。同时，对AlexNet模型进行了改进，利用数字双胞胎将真实空间中的脑图像映射到虚拟空间。在此基础上，构建了基于半监督支持向量机和改进AlexNet的脑图像融合数字双胞胎诊断预测模型。通过采集某医院脑肿瘤科的磁共振成像(MRI)数据，通过仿真实验验证了所建模型的性能。包括一些最先进的模型用于性能比较：长短期记忆(LSTM)、卷积神经网络(CNN)、递归神经网络(RNN)、AlexNet和多层感知器(MLP)。实验结果表明，该模型的特征识别和提取正确率达到92.52%，与其他模型相比至少提高了2.76%。训练时间约为10 0 S，测试时间约为0.68 S，模型的均方根误差和平均绝对误差分别为4.91%和5.59%。对于脑图像分割和融合的评价指标，该模型可以提供79.55%的Jaccard系数、90.43%的正预测值(PPV)、73.09%的灵敏度和75.58%的Dice相似性系数(DSC)，明显优于其他模型。加速效率分析表明，改进的AlexNet模型适用于处理海量脑图像数据，具有较高的加速比指标。综上所述，所构建的模型具有较高的准确率、较好的加速效率、较好的分割和识别性能，同时保证了较低的误差，为脑图像特征识别和数字诊断提供了实验依据。
The purpose is to explore the feature recognition, diagnosis, and forecasting performances of Semi-Supervised Support Vector Machines (S3VMs) for brain image fusion Digital Twins (DTs). Both unlabeled and labeled data are used regarding many unlabeled data in brain images, and semi supervised support vector machine (SVM) is proposed. Meantime, the AlexNet model is improved, and the brain images in real space are mapped to virtual space by using digital twins. Moreover, a diagnosis and prediction model of brain image fusion digital twins based on semi supervised SVM and improved AlexNet is constructed. Magnetic Resonance Imaging (MRI) data from the Brain Tumor Department of a Hospital are collected to test the performance of the constructed model through simulation experiments. Some state-of-art models are included for performance comparison: Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), AlexNet, and Multi-Layer Perceptron (MLP). Results demonstrate that the proposed model can provide a feature recognition and extraction accuracy of 92.52%, at least an improvement of 2.76% compared to other models. Its training lasts for about 100 s, and the test takes about 0.68 s. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of the proposed model are 4.91 and 5.59%, respectively. Regarding the assessment indicators of brain image segmentation and fusion, the proposed model can provide a 79.55% Jaccard coefficient, a 90.43% Positive Predictive Value (PPV), a 73.09% Sensitivity, and a 75.58% Dice Similarity Coefficient (DSC), remarkably better than other models. Acceleration efficiency analysis suggests that the improved AlexNet model is suitable for processing massive brain image data with a higher speedup indicator. To sum up, the constructed model can provide high accuracy, good acceleration efficiency, and excellent segmentation and recognition performances while ensuring low errors, which can provide an experimental basis for brain image feature recognition and digital diagnosis.
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