scholarly journals Intelligent Algorithm-Based Picture Archiving and Communication System of MRI Images and Radiology Information System-Based Medical Informatization

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Biao Liu ◽  
Baogao Tan ◽  
Lidi Huang ◽  
Jingxin Wei ◽  
Xulin Mo ◽  
...  
Keyword(s):  
Neural Network ◽  
Information System ◽  
Medical Information ◽  
Rbf Neural Network ◽  
Classification Performance ◽  
Medical Information System ◽  
Regular Group ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Picture Archiving And Communication

Objective. The study aimed to explore the application value of picture archiving and communication system (PCAS) of MRI images based on radial basis function (RBF) neural network algorithm combined with the radiology information system (RIS). Methods. 551 patients who required MRI examination in a hospital from May 2016 to May 2021 were selected as research subjects. Patients were divided into two groups according to their own wishes. Those who were willing to use the RBF neural network algorithm-based PCAS of MRI images combined with RIS were set as the combined group, involving a total of 278 cases; those who were unwilling were set as the regular group, involving a total of 273 cases. The RBF neural network algorithm-based PCAS of MRI images combined with RIS was trained and tested for classification performance and then used for comparison analysis. Result. The actual output (0.031259–0.038515) of all test samples was almost the same as the target output (0.000000) ( P  > 0.05). In the first 50,000 learnings, the iteration error of the RBF neural network dropped rapidly and finally stabilized at 0.038. The classification accuracy of the RBF neural network algorithm-based PCAS of MRI images combined with RIS for the head was 94.28%, that of abdomen was 97.22%, and it was 93.10% for knee joint, showing no statistically significant differences ( P  > 0.05), and the total classification accuracy was as high as 95%. The time spent in the examination in the combined group was about 2 hours, and that in the regular group was about 4 hours ( P  > 0.05). The satisfaction of the combined group (96.76%) was significantly higher than that of the control group (46.89%) ( P  > 0.05). Conclusion. The RBF neural network has good classification performance for MRI images. To incorporate intelligent algorithms into the medical information system can optimize the system. RBF has good application prospects in the medical information system, and it is worthy of continuous exploration.

Software Design and Implementation of Large-Scale Intelligent Traffic Monitoring System

2014 ◽  
Vol 644-650 ◽  
pp. 1351-1354
Author(s):  
Jun Ye Wang
Keyword(s):  
Neural Network ◽  
Monitoring System ◽  
Large Scale ◽  
Rbf Neural Network ◽  
Traffic Monitoring ◽  
Monitoring Methods ◽  
Monitoring Method ◽  
Traffic State ◽  
Network Algorithm ◽  
Neural Network Algorithm

The design method of large-scale intelligent traffic monitoring system is studied. Traffic monitoring methods have become the core problem of intelligent transportation research field. To this end, this paper proposes an intelligent traffic monitoring method based on clustering RBF neural network algorithm. Fourier coefficient normalization method is used to extract the feature of traffic state, to be as the basis for intelligent traffic monitoring. Using clustering RBF neural network algorithm identify the traffic state effectively, thus to complete the state recognition of intelligent traffic monitoring. Experimental results show that the proposed algorithm performed in intelligent traffic monitoring, can greatly improve the accuracy of monitoring.

scholarly journals Multi-Source Deep Transfer Neural Network Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3992 ◽  
Author(s):  
Jingmei Li ◽  
Weifei Wu ◽  
Di Xue ◽  
Peng Gao
Keyword(s):  
Neural Network ◽  
Probability Distribution ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Classification Performance ◽  
Classification Error ◽  
Target Domain ◽  
Source Domain ◽  
Network Algorithm ◽  
Neural Network Algorithm

Transfer learning can enhance classification performance of a target domain with insufficient training data by utilizing knowledge relating to the target domain from source domain. Nowadays, it is common to see two or more source domains available for knowledge transfer, which can improve performance of learning tasks in the target domain. However, the classification performance of the target domain decreases due to mismatching of probability distribution. Recent studies have shown that deep learning can build deep structures by extracting more effective features to resist the mismatching. In this paper, we propose a new multi-source deep transfer neural network algorithm, MultiDTNN, based on convolutional neural network and multi-source transfer learning. In MultiDTNN, joint probability distribution adaptation (JPDA) is used for reducing the mismatching between source and target domains to enhance features transferability of the source domain in deep neural networks. Then, the convolutional neural network is trained by utilizing the datasets of each source and target domain to obtain a set of classifiers. Finally, the designed selection strategy selects classifier with the smallest classification error on the target domain from the set to assemble the MultiDTNN framework. The effectiveness of the proposed MultiDTNN is verified by comparing it with other state-of-the-art deep transfer learning on three datasets.

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