COVID-19 Classification Based on Deep Convolution Neural Network Over a Wireless Network

Abstract

Corona Virus Disease 19 (COVID-19) firstly spread in China since December 2019. Then, it spread at a high rate around the world. Therefore, rapid diagnosis of COVID-19 has become a very hot research topic. One of the possible diagnostic tools is to use a deep convolution neural network (DCNN) to classify patient images. Chest X-ray is one of the most widely-used imaging techniques for classifying COVID-19 cases. This paper presents a proposed wireless communication and classification system for X-ray images to detect COVID-19 cases. Different modulation techniques are compared to select the most reliable one with less required bandwidth. The proposed DCNN architecture consists of deep feature extraction and classification layers. Firstly, the proposed DCNN hyper-parameters are adjusted in the training phase. Then, the tuned hyper-parameters are utilized in the testing phase. These hyper-parameters are the optimization algorithm, the learning rate, the mini-batch size and the number of epochs. From simulation results, the proposed scheme outperforms other related pre-trained networks. The performance metrics are accuracy, loss, confusion matrix, sensitivity, precision, score, specificity, Receiver Operating Characteristic (ROC) curve, and Area Under the Curve (AUC). The proposed scheme achieves a high accuracy of 97.8 %, a specificity of 98.5 %, and an AUC of 98.9 %.

Design of Cloud Storage-Oriented Sports Physical Fitness Monitoring System

“…Combined with developed Internet technology, advanced cluster application, and mature distributed file system, this technology enables the massive types of storage devices in the network to work together to form an overall storage and computing structure model and jointly provide data storage and computing services and interface access functions. is paper transmits the collected data to the cloud platform through the wireless network to realize the cloud storage function [16], which can realize the storage and calculation of massive sports fitness monitoring data. In addition, compared with embedding the algorithm into the sensor terminal module program, the data processing algorithm stored on the remote cloud platform can be continuously optimized and updated in real time to ensure the accuracy and expansibility of the data [17,18].…”