基于改进全卷积神经网络的医疗数据表面重建算法

为了实现对医疗数据的快速检测和分类识别，需要对医疗数据进行表面重建设计，首先，提出一种基于改进全卷积神经网络的医疗数据表面重建算法.采用无线射频识别技术进行医疗数据的大数据采样，对RFID采集的医疗数据进行信息融合处理，采用多元回归分析方法提取医疗数据的相关性统计特征量，然后，针对医疗数据中的冗余特征采用匹配滤波检测器进行冗余滤波处理，对提纯后的医疗数据采用相空间重构技术实现医疗数据重构，最后，对重构数据采用改进全卷积神经网络分类器进行分类识别，实现医疗数据的表面重建和自动识别.仿真结果表明，所提方法的医疗数据冗余特征处理效果较好，数据分类精度可高达90%以上，且医疗数据重建误差小，耗时少.

Surface Reconstruction Algorithm of Medical Data Based on Improved Total Convolution Neural Network

In order to realize the rapid detection and classification of medical data, the surface reconstruction design of medical data needs to be carried out. A surface reconstruction algorithm of medical data based on improved total convolution neural network is proposed. The big data sampling of medical data was carried out by using radio frequency identification technology, the medical data collected by RFID was processed by information fusion, and the correlation statistical characteristics of medical data were extracted by multiple regression analysis. According to the characteristics of medical data, matching filter detector is used for redundant filtering, and phase space reconstruction technology is used to reconstruct medical data after purification. In order to realize the surface reconstruction and automatic recognition of medical data, an improved total convolution neural network classifier is used to classify and recognize the reconstructed data. The simulation results show that the proposed method has a good effect on the redundant feature processing of medical data. The accuracy of data classification is more than 90%, and the error of medical data reconstruction is smaller and the time consuming is less.