Deep learning-based COVID-19 diagnosis using CT scans with laboratory and physiological parameters

The global economy has been dramatically impacted by COVID-19, which has spread to be a pandemic. COVID-19 virus affects the respiratory system, causing difficulty breathing in the patient. It is crucial to identify and treat infections as soon as possible. Traditional diagnostic reverse transcription-polymerase chain reaction (RT-PCR) methods require more time to find the infection. A high infection rate, slow laboratory analysis, and delayed test results caused the widespread and uncontrolled spread of the disease. This study aims to diagnose the COVID-19 epidemic by leveraging a modified convolutional neural network (CNN) to quickly and safely predict the disease's appearance from computed tomography (CT) scan images and a laboratory and physiological parameters dataset. A dataset representing 500 patients was used to train, test, and validate the CNN model with results in detecting COVID-19 having an accuracy, sensitivity, specificity, and F1-score of 99.33%, 99.09%, 99.52%, and 99.24%, respectively. These experimental results suggest that our strategy performs better than previously published approaches.

Hybridization of particle swarm optimization algorithm with neural network for COVID-19 using computerized tomography scan and clinical parameters

The 2019 coronavirus disease began in Wuhan, China, and spread worldwide. This pandemic was concerning, given its significant and worrying impact on human health. Strategies to manage the disease begin with diagnosing the infection, often using the real-time reverse transcription polymerase chain reaction (RT-PCR) assay. However, this process is time intensive. Therefore, alternative rapid methods to diagnose the coronavirus with high accuracy are needed. X-ray and computerized tomography (CT) scans are reasonable solutions for rapid coronavirus diagnosis. The dataset of 500 patients was tested, including 286 uninfected patients and 214 infected with COVID-19. Clinical parameters, including heart rate (HR), temperature (T), blood oxygen level, D-dimer, and CT scan, including red-green-blue (RGB) pixel values of the left and right lungs, were collected from 500 patients and used to train an artificial neural network (ANN) to diagnose coronavirus. The ANN was hybridized with a particle swarm optimization (PSO) algorithm to improve diagnosis accuracy. The results show that the proposed PSO-ANN method significantly improved diagnosis accuracy (98.93%), sensitivity (100%), and specificity (98.13%). The effectiveness of the proposed method was confirmed by comparing the findings with those of previous studies.

Improved Control System Based on PSO and ANN for Social Distancing for Patients With COVID-19

The World Health Organization has declared the COVID-19 pandemic, with most countries being affected by this virus both socially and economically. It thus became necessary to develop solutions to help monitor and control disease spread by controlling medical workers' movements and warning them against approaching infected individuals in isolation rooms. This paper introduces a control system that uses improved particle swarm optimization (PSO), and artificial neural network (ANN) approaches to achieve social distancing. The distance between medical workers carrying mobile nodes and the beacon node (isolation room) was determined using the ZigBee wireless protocol's received signal strength indicator (RSSI). Two path loss models were developed to determine the distance from patients with COVID-19: the first is a log-normal shading model (LNSM), and the second is a polynomial function (POL). The coefficient values of the POL model were controlled based on PSO to improve model performance. A random-nonlinear time variation controller-PSO (RNT-PSO) approach was developed to avoid the local minima of the conventional PSO. As a result, social distancing for COVID-19 can be accurately determined. The measured RSSI and the distance were used as ANN inputs, while three control signals (alarming, warning, and closing) were used as ANN outputs. The results revealed that the hybrid model between POL and RNT-PSO, called RNT-PSO-POL, improved the system's performance by reducing the mean absolute error of distance to 1.433 m, compared to 1.777 m for the LNSM. The results show that the ANN achieves robust performance in terms of mean squared error.

The Development of Energy Efficient Routing Protocol Based on SDN Architecture And Intelligent Technique For HIHC-IoT

Due to the COVID-19 virus infections that have occurred recently, the development of an intelligent healthcare protocol that considers emergent heart cases becomes indispensable. This protocol is based on the method that aims to monitor patients remotely by using Internet of Thing (IoT) devices, which do not select the nodes that are nearby the patient's or in the room to choose as a Clusters Head (CH). So on, the energy consumption of these devices will be reduced, because of their highest importance than the other non-medical ones. Accordingly, this paper proposes a method called High Importance Healthcare-Internet of Things (HIHC-IoT), which is suitable for the emergent healthcare conditions of the patient and the caregiver. Furthermore, WSNs have some issues that reduce system performance, such as resource limits for sensors that may affect power supply, memory, communication capacity, and processing units. In the proposed work, the optimum set of CHs has been selected depending on the residual energy, the distance between the nodes, and the HI nodes. In addition, cloud technology, SDN architecture, and an efficient intelligent algorithm called High Importance-Future Search Algorithm (HI-FSA) have been used. Finally, the compered result of normal protocols with the proposed intelligent protocol, showed an increase in network life by about 40% and about 22% for an optimized routing protocol and increasing the number of packets delivered between nodes. © 2021 IEEE.