Human retinal optic disc detection with grasshopper optimization algorithm

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dc.contributor.authorRahebi, Javad
dc.date.accessioned2022-04-08T07:14:51Z
dc.date.available2022-04-08T07:14:51Z
dc.date.issued2022en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractA growing number of qualified ophthalmologists are promoting the need to use computer-based retinal eye processing image recognition technologies. There are different methods and algorithms in retinal images for detecting optic discs. Much attention has been paid in recent years using intelligent algorithms. In this paper, in the human retinal images, we used the Grasshopper optimization algorithm to implement a new automated method for detecting an optic disc. The clever algorithm is influenced by the social nature of the grasshopper, the intelligent Grasshopper algorithm. Include this algorithm; the population contains the grasshoppers, each of which has a common luminance or exercise score. In this method, two-by-two insects are compared, so it could be shown that less attractive insects shift towards more attractive insects. Finally, one of the most attractive insects is selected, and this insect gives an optimum solution to the problem. Here, we used the light intensity of the retinal pixels instead of grasshopper illuminations. According to local variations, the effect of these insects also indicates different light intensity values in images. Since the brightest area "represents the optic disc in retinal images, all insects travel to the brightest area, which leads to the determined position for an optic disc in the image. The performance was evaluated on 210 images, reflecting three Open to the public and sequentially distributed datasets DIARETDB1 89 images, STARE 81 images, and DRIVE 40 images. The results of the proposed algorithm implementation give a 99.51% accuracy rate in the DiaRetDB1 dataset, 99.67% in the STARE dataset, and 99.62% in the DRIVE dataset. The results of the implementation show the strong capacity and accuracy of the proposed algorithm for detecting the optic disc from retinal images. Also, the recorded time required for (OD) detection in these images is180.14 s for the DiaRetDB1, 65.13s for STARE, and 80.64s for DRIVE, respectively. These are average values for the times.en_US
dc.identifier.citationAl Shalchi, N. F. A., & Rahebi, J. (2022). Human retinal optic disc detection with grasshopper optimization algorithm. Multimedia Tools and Applications, 1-19.en_US
dc.identifier.endpage19en_US
dc.identifier.scopus2-s2.0-85127183064
dc.identifier.scopusqualityQ1
dc.identifier.startpage1en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/2326
dc.identifier.wosWOS:000771379200001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAl Shalchi, Nassrallah Faris Abdukader
dc.language.isoen
dc.relation.ispartofMultimedia Tools and Applications
dc.relation.isversionof10.1007/s11042-022-12838-8en_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectGrasshopper Optimization Algorithmen_US
dc.subjectOptic Disc Detectionen_US
dc.subjectRetinal İmagesen_US
dc.titleHuman retinal optic disc detection with grasshopper optimization algorithm
dc.typeArticle

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