Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/1169
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dc.contributor.authorÖztürk, Şaban-
dc.contributor.authorÖzkaya, Umut-
dc.date.accessioned2021-12-13T10:34:47Z-
dc.date.available2021-12-13T10:34:47Z-
dc.date.issued2020-
dc.identifier.issn0897-1889-
dc.identifier.issn1618-727X-
dc.identifier.urihttps://doi.org/10.1007/s10278-020-00343-z-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/1169-
dc.description.abstractRecently, the incidence of skin cancer has increased considerably and is seriously threatening human health. Automatic detection of this disease, where early detection is critical to human life, is quite challenging. Factors such as undesirable residues (hair, ruler markers), indistinct boundaries, variable contrast, shape differences, and color differences in the skin lesion images make automatic analysis quite difficult. To overcome these challenges, a highly effective segmentation method based on a fully convolutional network (FCN) is presented in this paper. The proposed improved FCN (iFCN) architecture is used for the segmentation of full-resolution skin lesion images without any pre- or post-processing. It is to support the residual structure of the FCN architecture with spatial information. This situation, which creates a more advanced residual system, enables more precise detection of details on the edges of the lesion, and an analysis independent of skin color can be performed. It offers two contributions: determining the center of the lesion and clarifying the edge details despite the undesirable effects. Two publicly available datasets, the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 Challenge and PH2 datasets, are used to evaluate the performance of the iFCN method. The mean Jaccard index is 78.34%, the mean Dice score is 88.64%, and the mean accuracy value is 95.30% for the proposed method for the ISBI 2017 test dataset. Furthermore, the mean Jaccard index is 87.1%, the mean Dice score is 93.02%, and the mean accuracy value is 96.92% for the proposed method for the PH2 test dataset.en_US
dc.language.isoenen_US
dc.publisherSPRINGERen_US
dc.relation.ispartofJOURNAL OF DIGITAL IMAGINGen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSkin Lesion Segmentationen_US
dc.subjectCnnen_US
dc.subjectFcnen_US
dc.subjectSegmentationen_US
dc.subjectMelanomaen_US
dc.subjectDermoscopy Imagesen_US
dc.subjectBorder Detectionen_US
dc.titleSkin Lesion Segmentation with Improved Convolutional Neural Networken_US
dc.typeArticleen_US
dc.identifier.doi10.1007/s10278-020-00343-z-
dc.identifier.pmidPubMed: 32378058en_US
dc.identifier.scopus2-s2.0-85085100289en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.authoridOzturk, Saban/0000-0003-2371-8173-
dc.authorwosidOzturk, Saban/ABI-3936-2020-
dc.identifier.volume33en_US
dc.identifier.issue4en_US
dc.identifier.startpage958en_US
dc.identifier.endpage970en_US
dc.identifier.wosWOS:000531507800002en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.authorscopusid57191953654-
dc.authorscopusid57191610477-
dc.identifier.scopusqualityQ1-
item.openairetypeArticle-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.dept02.04. Department of Electrical and Electronics Engineering-
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collections
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections
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