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.

Predicting infection with coronavirus wirelessly based on artificial neural network and MATLAB

Due to the huge fast spread of Covid-19 around the world, which resulted in the loss of many lives, the maximum level of emergency was triggered all over the world. The best way to reduce COVID-19 infection is to prediction it early based on artificial intelligence (AI). To determine whether the patient has COVID-19 infection or not. An accurate and effective diagnosis system for Covid-19 was proposed in this paper. The diagnostic parameters for right and left lungs, D-dimer, and physiological parameters such as SpO2, temperature, and heart rate were collected from CT scans (three RGB colors) for right and left lungs, D-dimer, and physiological parameters such as SpO2, temperature, and heart rate. The data was collected from 300 patients, with each patient receiving 10 samples;114 of them were infected with Covid-19, while the remaining 186 were uninfected. For training, testing, and verifying the gathered data, an artificial neural network (ANN) on one hidden layer at 20 nodes-based Backpropagation method was used. For all diagnostic parameters, a total of 30,000 samples were obtained (300 patient x 10 parameters x 10 samples per patient). The 3,000 data samples (300 individuals, 10 samples each) were divided into three datasets: 70% for training ANN (2,100 out of 3,000 samples), 15% for testing ANN (450 out of 3,000 samples), and 15% for validation ANN (450 out of 3,000 samples) (450 out of 3,000 samples). In terms of ANN performance, correlation coefficient, error, mean absolute error (MAE), and histogram, the results of COVID-19 diagnosis based on ANN were studied. The MAE for training, validation, and testing at 20 nodes, respectively, was 0.0012, 0.012, and 0.013, indicating that the ANN achieves good diagnostic accuracy. In training, validation, and testing at 20 nodes, the correlation coefficient (R2) between the actual and estimated value was 0.9999, 0.9996, and 0.9998, respectively. In terms of correlation coefficient and MAE, the suggested technique beat the current state-of-The-Art. © 2023 Author(s).

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.

DIAGNOSIS of COVID-19 BASED on ARTIFICIAL INTELLIGENCE MODELS and PHYSIOLOGICAL SENSORS: REVIEW

Covid-19 invaded the world very quickly and caused the loss of many lives;maximum emergency was activated all over the world due to its rapid spread. Consequently, it became a huge burden on emergency and intensive care units due to the large number of infected individuals and the inability of the medical staff to deal with patients according to the degree of severity. Covid-19 can be diagnosed based on the artificial intelligence (AI) model. Based on AI, the CT images of the patient's chest can be analyzed to identify the patient case whether it is normal or he/she has Covid-19. The possibility of employing physiological sensors such as heart rate, temperature, respiratory rate, and SpO2 sensors in diagnosing Covid-19 was investigated. In this paper, several articles which used intelligent techniques and vital signs for diagnosing Covid-19 have been reviewed, classified, and compared. The combination of AI and physiological sensors reading, called AI-PSR, can help the clinician in making the decisions and predicting the occurrence of respiratory failure in Covid-19 patients. The physiological parameters of the Covid-19 patients can be transmitted wirelessly based on a specific wireless technology such as Wi-Fi and Bluetooth to the clinician to avoid direct contact between the patient and the clinician or nursing staff. The outcome of the AI-PSR model leads to the probability of recording and linking data with what will happen later, to avoid respiratory failure, and to help the patient with one of the mechanical ventilation devices.