Robust optimization of SegNet hyperparameters for skin lesion segmentation

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dc.authoridALPASLAN, Nuh/0000-0002-6828-755X
dc.authoridHanbay, Davut/0000-0003-2271-7865
dc.authoridSAHIN, Nurullah/0000-0002-3578-9959
dc.authorwosidALPASLAN, Nuh/AAA-4227-2022
dc.authorwosidHanbay, Davut/AAG-8511-2019
dc.contributor.authorSahin, Nurullah
dc.contributor.authorAlpaslan, Nuh
dc.contributor.authorHanbay, Davut
dc.date.accessioned2024-08-04T20:50:17Z
dc.date.available2024-08-04T20:50:17Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractMelanoma is considered the deadliest form of skin cancer, and the number of cases is increasing day by day. The early diagnosis of melanoma is critical, as it significantly increases the patient's chance of survival. However, distinguishing melanoma from other skin lesion types by the physician can be a complicated process due to the diversity of its structural and textural features. Numerous computer-aided diagnosis (CAD) systems have been developed to assist the physician in detecting melanoma during recent years. The segmentation is a critical step for CAD systems, as it directly contributes to the performance of both feature extraction and classification steps. The optimization of the hyperparameters of deep learning methods is a challenging research topic. In this paper, the Bayesian optimized SegNet approach is proposed for precise skin lesion segmentation. The proposed method is obtained competitive results with the latest skin lesion segmentation methods. The hyperparameters optimized SegNet has achieved the best results with the average Jaccard Index of 84.9 on ISBI2016 and 74.5 on ISBI2017 dataset. Experimental results indicate the validity of Bayesian optimized SegNet. In this study, it has been observed that the bayesian hyperparameter optimization in the SegNet, which is the latest deep learning architecture, increased the segmentation performance of the SegNet by 16% in the ISBI2016 dataset and by 7% in the ISBI2017 dataset.en_US
dc.identifier.doi10.1007/s11042-021-11032-6
dc.identifier.endpage36051en_US
dc.identifier.issn1380-7501
dc.identifier.issn1573-7721
dc.identifier.issue25en_US
dc.identifier.scopus2-s2.0-85107379744en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage36031en_US
dc.identifier.urihttps://doi.org/10.1007/s11042-021-11032-6
dc.identifier.urihttps://hdl.handle.net/11616/99971
dc.identifier.volume81en_US
dc.identifier.wosWOS:000655960200001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofMultimedia Tools and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMelanomaen_US
dc.subjectSegmentationen_US
dc.subjectSkin lesionen_US
dc.subjectSegNeten_US
dc.subjectBayesian optimizationen_US
dc.titleRobust optimization of SegNet hyperparameters for skin lesion segmentationen_US
dc.typeArticleen_US

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