Triple-Stream Deep Feature Selection with Metaheuristic Optimization and Machine Learning for Multi-Stage Hypertensive Retinopathy Diagnosis

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dc.contributor.authorSuyun, Suleyman Burcin
dc.contributor.authorYurdakul, Mustafa
dc.contributor.authorTasdemir, Sakir
dc.contributor.authorBilis, Serkan
dc.date.accessioned2026-04-25T14:20:27Z
dc.date.available2026-04-25T14:20:27Z
dc.date.issued2025
dc.departmentSinop Üniversitesi
dc.description.abstractHypertensive retinopathy (HR) is a serious eye disease that can lead to permanent vision loss if not diagnosed early. The conventional diagnostic methods are subjective and time-consuming, so there is a need for an automated and reliable system. In this study, a three-stage method that provides high accuracy in HR diagnosis is proposed. In the first stage, 14 well-known Convolutional Neural Network (CNN) models were evaluated, and the top three models were identified. Among these models, DenseNet169 achieved the highest accuracy rate of 87.73%. In the second stage, the deep features obtained from these three models were combined and classified using machine learning (ML) algorithms including Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The SVM with a sigmoid kernel achieved the best performance (92% accuracy). In the third stage, feature selection was performed using metaheuristic optimization techniques including Genetic Algorithm (GA), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Harris Hawk Optimization (HHO). The HHO algorithm increased the classification accuracy to 94.66%, enhancing the model's generalization ability and reducing misclassifications. The proposed method provides superior accuracy in the diagnosis of HR at different severity levels compared to single-model CNN approaches. These results demonstrate that the integration of Deep Learning (DL), ML, and optimization techniques holds significant potential in automated HR diagnosis.
dc.identifier.doi10.3390/app15126485
dc.identifier.issn2076-3417
dc.identifier.issue12
dc.identifier.orcid0000-0003-2808-1861
dc.identifier.orcid0000-0002-2433-246X
dc.identifier.orcid0000-0003-0562-4931
dc.identifier.scopus2-s2.0-105008948356
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/app15126485
dc.identifier.urihttps://hdl.handle.net/11486/8578
dc.identifier.volume15
dc.identifier.wosWOS:001515114400001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofApplied Sciences-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260420
dc.subjectConvolutional Neural Network
dc.subjecteye disease
dc.subjectfeature fusion
dc.subjectHarris Hawk Optimization
dc.subjecthypertensive retinopathy
dc.titleTriple-Stream Deep Feature Selection with Metaheuristic Optimization and Machine Learning for Multi-Stage Hypertensive Retinopathy Diagnosis
dc.typeArticle

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