Ant Colony and Whale Optimization Algorithms Aided by Neural Networks for Optimum Skin Lesion Diagnosis: A Thorough Review

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dc.authoridHameed, Alaa Ali/0000-0002-8514-9255
dc.authoridSyafrudin, Muhammad/0000-0002-5640-4413
dc.authoridFitriyani, Norma Latif/0000-0002-1133-3965
dc.contributor.authorMukhlif, Yasir Adil
dc.contributor.authorRamaha, Nehad T. A.
dc.contributor.authorHameed, Alaa Ali
dc.contributor.authorSalman, Mohammad
dc.contributor.authorYon, Dong Keon
dc.contributor.authorFitriyani, Norma Latif
dc.contributor.authorSyafrudin, Muhammad
dc.date.accessioned2024-05-19T14:46:40Z
dc.date.available2024-05-19T14:46:40Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractThe adoption of deep learning (DL) and machine learning (ML) has surged in recent years because of their imperative practicalities in different disciplines. Among these feasible workabilities are the noteworthy contributions of ML and DL, especially ant colony optimization (ACO) and whale optimization algorithm (WOA) ameliorated with neural networks (NNs) to identify specific categories of skin lesion disorders (SLD) precisely, supporting even high-experienced healthcare providers (HCPs) in performing flexible medical diagnoses, since historical patient databases would not necessarily help diagnose other patient situations. Unfortunately, there is a shortage of rich investigations respecting the contributory influences of ACO and WOA in the SLD classification, owing to the recent adoption of ML and DL in the medical field. Accordingly, a comprehensive review is conducted to shed light on relevant ACO and WOA functionalities for enhanced SLD identification. It is hoped, relying on the overview findings, that clinical practitioners and low-experienced or talented HCPs could benefit in categorizing the most proper therapeutical procedures for their patients by referring to a collection of abundant practicalities of those two models in the medical context, particularly (a) time, cost, and effort savings, and (b) upgraded accuracy, reliability, and performance compared with manual medical inspection mechanisms that repeatedly fail to correctly diagnose all patients.en_US
dc.description.sponsorshipNational Research Foundation of Koreaen_US
dc.description.sponsorshipNo Statement Availableen_US
dc.identifier.doi10.3390/math12071049
dc.identifier.issn2227-7390
dc.identifier.issue7en_US
dc.identifier.scopus2-s2.0-85190115806en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org10.3390/math12071049
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5571
dc.identifier.volume12en_US
dc.identifier.wosWOS:001201212400001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofMathematicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectSkin Lesion Disorder (Sld)en_US
dc.subjectAnt Colony Optimization (Aco)en_US
dc.subjectWhale Optimization Algorithm (Woa)en_US
dc.subjectNeural Networks (Nns)en_US
dc.subjectFeature Selectionen_US
dc.subjectClassificationen_US
dc.titleAnt Colony and Whale Optimization Algorithms Aided by Neural Networks for Optimum Skin Lesion Diagnosis: A Thorough Reviewen_US
dc.typeReview Articleen_US

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