Jaya‐tunicate swarm algorithm based generative adversarial network for COVID‐19 prediction with chest computed tomography images

• Published in: Concurrency and computation Vol. 34; no. 23; pp. e7211 - n/a
• Main Authors: Doraiswami, Palanivel Rajan, Sarveshwaran, Velliangiri, Swamidason, Iwin Thanakumar Joseph, Sorna, Sona Chandra Devadass
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 25.10.2022; Wiley Subscription Services, Inc
• Subjects: Algorithms; Chest; Clustering; Computed tomography; computed tomography (CT); COVID-19; Feature extraction; generative adversarial network (GAN); Generative adversarial networks; Jaya algorithm; Lungs; Tomography; Training; tunicate swarm algorithm (TSA)
• ISSN: 1532-0626; 1532-0634; 1532-0634
• DOI: 10.1002/cpe.7211

Summary A novel corona virus (COVID‐19) has materialized as the respiratory syndrome in recent decades. Chest computed tomography scanning is the significant technology for monitoring and predicting COVID‐19. To predict the patients of COVID‐19 at early stage poses an open challenge in the research community. Therefore, an effective prediction mechanism named Jaya‐tunicate swarm algorithm driven generative adversarial network (Jaya‐TSA with GAN) is proposed in this research to find patients of COVID‐19 infections. The developed Jaya‐TSA is the incorporation of Jaya algorithm with tunicate swarm algorithm (TSA). However, lungs lobs are segmented using Bayesian fuzzy clustering, which effectively find the boundary regions of lung lobes. Based on the extracted features, the process of COVID‐19 prediction is accomplished using GAN. The optimal solution is obtained by training GAN using proposed Jaya‐TSA with respect to fitness measure. The dimensionality of features is reduced by extracting the optimal features, which enable to increase the speed of training process. Moreover, the developed Jaya‐TSA based GAN attained outstanding effectiveness by considering the factors, like, specificity, accuracy, and sensitivity that captured the importance as 0.8857, 0.8727, and 0.85 by varying training data.