Title: Fishier mantis optimiser: a swarm intelligence algorithm for clustering images of COVID-19 pandemic

Authors: Javad Rahebi

Addresses: Department of Software Engineering, Istanbul Topkapi University, Istanbul, Turkey

Abstract: In this study, an automated segmentation method is used to increase the speed of diagnosis and reduce the segmentation error of CT scans of the lung. In the proposed technique, the fishier mantis optimiser (FMO) algorithm is modelling and formulated based on the intelligent behaviour of mantis insects for hunting to create an intelligent algorithm for image segmentation. In the second phase of the proposed method, the proposed algorithm is used to cluster scanned image images of COVID-19 patients. Implementation of the proposed technique on CT scan images of patients shows that the similarity index of the proposed method is 98.36%, accuracy is 98.45%, and sensitivity is 98.37%. The proposed algorithm is more accurate in diagnosing COVID-19 patients than the falcon algorithm, the spotted hyena optimiser (SHO), the Grasshopper optimisation algorithm (GOA), the grey wolf optimisation algorithm (GWO), and the black widow optimisation algorithm (BWO).

Keywords: meta-heuristic algorithms; FMO; fishier mantis optimiser; COVID 19 disease; coronavirus; clustering.

DOI: 10.1504/IJNT.2023.131111

International Journal of Nanotechnology, 2023 Vol.20 No.1/2/3/4, pp.25 - 49

Received: 04 May 2021
Accepted: 03 Aug 2021
Published online: 31 May 2023 *