A manta ray-bayesian optimization approach for hyperparameter-tuned convolutional neural networks in lung cancer classification

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dc.contributor.authorSamal, Sonali
dc.contributor.authorSunder, Shyam
dc.contributor.authorGadekellu, Thippa Reddy
dc.contributor.authorYagin, Fatma Hilal
dc.contributor.authorEl Shawi, Radwa
dc.contributor.authorAhmed, Nada
dc.date.accessioned2026-06-19T06:39:34Z
dc.date.available2026-06-19T06:39:34Z
dc.date.issued2026
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractLung cancer remains a global health challenge that is unavoidable. Despite the advances in lung cancer classification using deep learning models, the performance remains highly dependent on hyperparameter selection, whereas conventional grid or random search methods are often computationally inefficient in high-dimensional spaces. So, to address the issue, this paper presents a Convolutional Neural Network(CNN) which is hybridized by dual stage hyperparameter optimization techniques for lung cancer image classification. The approach integrates Bayesian Optimization (BO) and Manta Ray Foraging Optimization (MRFO) to efficiently explore and fine-tune a defined hyperparameter search space, including convolution filter count, learning rate, dense layer neurons, and dropout rate. Initially, Bayesian Optimization explores the search space by modeling the objective function with a Gaussian Process and selecting candidate hyperparameters via the Expected Improvement criterion. The best solution obtained is then further enhanced using MRFO, which incrementally refines the parameters through its chain, cyclone, and somersault foraging mechanisms. This two-step process strikes a balance between exploring the world and taking advantage of local resources. The CNN trained with the optimized hyperparameters achieved good accuracy in lung cancer image classification, demonstrating the potency of combining probabilistic modeling with bio-inspired optimization. Experimental results show that the proposed hybrid CNN method has a testing accuracy of 98%, which is better than that of many cutting-edge models. The results show that metaheuristic-based optimization could be useful in deep learning applications, especially in medical image analysis.
dc.description.sponsorshipThis work is supported by the European Regional Development Funds via the MobilitasPlus programme (grant MOBTT75). This work is supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2026R756), Princess Nourah [MOBTT75]
dc.description.sponsorshipThis is funded by the project Increasing the knowledge intensity of Ida-Viru entrepreneurship co-funded by the European Union. This work is supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2026R756), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
dc.identifier.doi10.1038/s41598-026-42506-y
dc.identifier.issn2045-2322
dc.identifier.issue1
dc.identifier.pmid41794961
dc.identifier.scopus2-s2.0-105038836319
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1038/s41598-026-42506-y
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5685
dc.identifier.volume16
dc.identifier.wosWOS:001765074400001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherNature Portfolio
dc.relation.ispartofScientific Reports
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260612
dc.subjectLung Cancer
dc.subjectDeep Learning
dc.subjectBayesian Optimization
dc.subjectManta Ray Optimization
dc.titleA manta ray-bayesian optimization approach for hyperparameter-tuned convolutional neural networks in lung cancer classification
dc.typeArticle

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