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.

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.

The Covid-19 epidemic appeared suddenly, with a rapid start and leaping steps, declaring a threat to global health where it was the beginnings of its upbringing in Wuhan, China. Where the World Health Organization announced after confirming the results of human infections in December 2019 that it hurts all aspects of life in general and human health in particular. Therefore, it requires addressing such an epidemic quickly and with tight steps to avoid aggravating the situation, especially the lack of appropriate treatment. The necessity necessitated the use of quarantine for the injured and social distancing, in addition to the use of preventive measures such as masks, hand sterilization, non-contact, and leaving a safe distance. This paper aims to use an ANN algorithm based on CT and some laboratory and clinical parameters to determine whether a person is infected with Covid-19 or not. The results showed that two hidden layers were chosen for the ANN algorithm, where the first hidden layer was installed with ten nodes, while the second hidden layer was selected with five nodes once, ten nodes again, fifteen nodes, and twenty nodes. The results showed the best two hidden layers 10-20 nodes, and the accuracy was 99.43%.