Research on the improvement of the diagnostic effect of machine learning on nuclear magnetic resonance of brain tumors

Research on classification algorithm of cerebral small vessel disease based on convolutional neural network

The investigation on brain magnetic resonance imaging (MRI) of cerebral small vessel disease (CSVD) classification algorithm based on deep learning is particularly important in medical image analyses and has not been reported. This paper proposes an MRI classification algorithm based on convolutional neural network (MRINet), for accurately classifying CSVD and improving the classification performance. The working method includes five main stages: fabricating dataset, designing network model, configuring the training options, training model and testing performance. The actual training and testing datasets of MRI of CSVD are fabricated, the MRINet model is designed for extracting more detailedly features, a smooth categorical-cross-entropy loss function and Adam optimization algorithm are adopted, and the appropriate training parameters are set. The network model is trained and tested in the fabricated datasets, and the classification performance of CSVD is fully investigated. Experimental results show that the loss and accuracy curves demonstrate the better classification performance in the training process. The confusion matrices confirm that the designed network model demonstrates the better classification results, especially for luminal infarction. The average classification accuracy of MRINet is up to 80.95% when classifying MRI of CSVD, which demonstrates the superior classification performance over others. This work provides a sound experimental foundation for further improving the classification accuracy and enhancing the actual application in medical image analyses.

3D Printing in Cranioplasty for Giant Cranial Deformity with Multi-Dimensional Nuclear Magnetic Simulation

This article is based on 3D printing technology, through multi-dimensional nuclear magnetic stimulation to the in-depth study of the application of plastic surgery in patients with giant cranial deformity cranial reduction, first of all, patients with CT scan of the brain, based on CT data for 3D reconstruction, 3D geometric modeling, using 3D printing Prepare multiple skull 1:1 scale, solid models, perform surgical planning and drills, determine the surgical plan (related parameters such as surgical time, cranial cavity volume, frontal plane ratio, anterior-posterior diameter, left-right diameter, head-to-height ratio, etc.), it can increase the patient’s speed and stride, and complete a variety of material tests. The 3D printing group had lower pain VAS scores at 1 h and 24 h after surgery than the traditional data group. The same data observed from different dimensions may yield different results, but also enable people to understand the nature of things more comprehensively and clearly. It was statistically significant (P < 0.05). The postoperative swelling of the 3D printing group was less than that of the customary group, and the difference was statistically significant (P < 0.05). Through 12 months of follow up observation, the power of 3D printing is higher than that of the habitual group, and the difference is statistically significant (P < 0.05). This technology has an important guiding significance in future related treatment technology.