Fishier mantis optimiser: a swarm intelligence algorithm for clustering images of COVID-19 pandemic
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).
| Yazar |
Javad Rahebi |
| Yayın Türü | Makale |
| Tek Biçim Adres | https://hdl.handle.net/20.500.14081/1905 |
| Konu Başlıkları |
meta-heuristic algorithms
FMO fishier mantis optimiser COVID 19 disease coronavirus clustering |
| Koleksiyonlar |
Fakülteler Mühendislik Fakültesi |
| Dergi | International Journal of Nanotechnology |
| Dergi Cilt | 20 |
| Dergi Sayısı | 1-4 |
| Sayfalar | 25 - 45 |
| Yayın Yılı | 2023 |
| Eser Adı [dc.title] | Fishier mantis optimiser: a swarm intelligence algorithm for clustering images of COVID-19 pandemic |
| Yazar [dc.contributor.author] | Javad Rahebi |
| Yayın Yılı [dc.date.issued] | 2023 |
| Yayın Türü [dc.type] | Makale |
| Özet [dc.description.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). |
| Açık Erişim Tarihi [dc.date.available] | 2023-05-23 |
| Yayıncı [dc.publisher] | International Journal of Nanotechnology |
| Dil [dc.language.iso] | English |
| Konu Başlıkları [dc.subject] | meta-heuristic algorithms |
| Konu Başlıkları [dc.subject] | FMO |
| Konu Başlıkları [dc.subject] | fishier mantis optimiser |
| Konu Başlıkları [dc.subject] | COVID 19 disease |
| Konu Başlıkları [dc.subject] | coronavirus |
| Konu Başlıkları [dc.subject] | clustering |
| Tek Biçim Adres [dc.identifier.uri] | https://hdl.handle.net/20.500.14081/1905 |
| ISSN [dc.identifier.issn] | 1475-7435 |
| Dergi [dc.relation.journal] | International Journal of Nanotechnology |
| Dergi Sayısı [dc.identifier.issue] | 1-4 |
| DOI [dc.identifier.doi] | 10.1504/IJNT.2023.131111 |
| Orcid [dc.identifier.orcid] | 0000-0001-9875-4860 |
| Bitiş Sayfası [dc.identifier.endpage] | 45 |
| Başlangıç Sayfası [dc.identifier.startpage] | 25 |
| Dergi Cilt [dc.identifier.volume] | 20 |
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