Research on Medical Data Feature Extraction and Intelligent Recognition Technology Based on Convolutional Neural Network

Abstract

In order to mine information from medical health data and develop intelligent applicationrelated issues, the multi-modal medical health data feature representation learning related content was studied, and several feature learning models were proposed for disease risk assessment. In the aspect of medical text feature learning, a medical text feature learning model based on convolutional neural network is proposed. The convolutional neural network text analysis technology is applied to the disease risk assessment application. The medical data feature representation adopts the deep learning method. The learning and extraction of different disease characteristics use the same method to realize the versatility of the model. A simple preprocessing of the experimental data samples, including its power frequency denoising and lead convolution regularization, constructs a convolutional neural network for medical data feature advancement and intelligent recognition. On the basis of it, several sets of experiments were carried out to discuss the influence of the convolution kernel and the choice of learning rate on the experimental results. In addition, comparative experiments with support vector machine, BP neural network and RBF neural network are carried out. The results show that the convolutional neural network used in this paper shows obvious advantages in recognition rate and training speed compared with other methods. In the aspect of time series data feature learning, a multi-channel convolutional self-encoding neural network is proposed. Analyze the connection between fatigue and emotional abnormalities and define the concept of emotional fatigue. The proposed multi-channel convolutional neural network is used to learn the data features, and the convolutional self-encoding neural network is used to learn the facial image data features. These two characteristics and the collected physiological data are combined to perform emotional fatigue detection. An emotional fatigue detection demonstration platform for multi-modal data feature fusion is established to realize data acquisition, emotional fatigue detection and emotional feedback. The experimental results verify the validity, versatility and stability of the model.

A Deep Learning Trained by Genetic Algorithm to Improve the Efficiency of Path Planning for Data Collection With Multi-UAV

“…There are up to 20 sensor nodes in the scenario ( = 20), and the minimum number of data nodes is 5 and the maximum number of data nodes is 20 ( ∈ [5,20]). The size of the scenario is set as 5000 × 5000 [28] ( = 5000 , = 5000 ).…”

“…States of the scenario and the path sections are the input features and labels of neural network. We choose convolution neural network (CNN) to study experiences because its superior features extraction capacity [20], [21], [22], [23]. Before training, the input features should be preprocessed including normalization and reshaping, which are given as follows.…”

Synthetic Data Generation System for AI-Based Diabetic Foot Diagnosis

“…Another problem with this approach is that the synthesized data fails to reflect the noisy nature of the data collected by wearable sensors, which we attempted to remedy with this research [9]. Data collected by wearable sensors is easily distorted by both external and internal factors [19]. This leads the data collected in this manner to display different characteristics compared to medical data procured using traditional methods using medical precision instruments [20].…”

Classification and Prediction of Tibetan Medical Syndrome Based on the Improved BP Neural Network

“…As can be seen from the test results, artificial neural network is a useful tool for cry classification. Liu et al [17] proposed a variety of feature learning models for disease analysis and evaluation. Based on convolutional neural network, a medical text feature learning model was proposed for disease risk assessment, and deep learning method was used for medical data feature analysis.…”