Abstract
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.
Highlights
That science and technology advances quickly, Artificial Intelligence (AI), Big Data (BD), the Internet of Things (IoT), and Deep Learning (DL) have been accepted in all walks of life
To explore the forecasting performance of the proposed model, Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), AlexNet, and Multi-Layer Perceptron (MLP) are included for comparison
The proposed brain image Digital Twins (DTs) diagnosis and forecasting model based on S3VMs and improved AlexNet can provide excellent recognition and forecasting accuracy
Summary
That science and technology advances quickly, Artificial Intelligence (AI), Big Data (BD), the Internet of Things (IoT), and Deep Learning (DL) have been accepted in all walks of life. Combining semisupervised learning and Support Vector Machines (SVMs) based on labeled samples obtained by time-domain simulation and the unlabeled samples from the actual system to assess brain image data can extract actual data distribution comprehensively and improve the weak generalization ability of present supervised feature extraction and assessment models, in an effort to enhance model’s adaptability to the actual system. Brain Image Fusion DTs Diagnosis and Forecasting Model Based on S3VMs and Improved AlexNet. Digital Twins (Sun et al, 2020) can forecast and analyze data of the physical space in the corresponding virtual space regarding the smart city development in physical spaces. In Eqs. 28–31), T represents the expert segmentation result, and P denotes the segmentation result of the proposed model based on S3VMs and the improved AlexNet
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