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

• Published in: Journal of engineering (Stevenage, England) Vol. 2023; no. 2
• Main Authors: Sameer, Humam Adnan, Gharghan, Sadik Kamel, Mutlag, Ammar Hussein
• Format: Journal Article
• Language: English
• Published: London John Wiley & Sons, Inc 01.01.2023
• Subjects: Accuracy; Algorithms; ANN; Artificial intelligence; Artificial neural networks; clinical parameters; Computed tomography; Coronaviruses; COVID-19; CT scan; Deep learning; Diagnosis; Disease prevention; Disease transmission; D‐dimer; Genomes; Heart rate; Infections; Laboratories; Medical imaging; Neural networks; Pandemics; Parameters; Particle swarm optimization; Patients; Pneumonia; Polymerase chain reaction; Principal components analysis; PSO; Respiratory failure; Support vector machines; Tomography; Viral diseases; X-rays
• ISSN: 2051-3305; 2051-3305
• DOI: 10.1049/tje2.12226

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. This study tested the dataset of 500 patients, including 286 uninfected patients and 214 infected with COVID‐19. Clinical parameters, including heart rate, temperature, blood oxygen level, D‐dimer, and CT images, consisting of red‐green‐blue pixel valuesof the left and right lungs, were collected and used to train and artificial neural network (ANN) to diagonse Coronavirus. The ANN was hybridized with partical swarm optimization (PSO) to improve diagnosis accuracy.