Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm

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dc.contributor.authorUnver, Halil Murat
dc.contributor.authorAyan, Enes
dc.date.accessioned2020-06-25T18:30:38Z
dc.date.available2020-06-25T18:30:38Z
dc.date.issued2019
dc.departmentKırıkkale Üniversitesi
dc.description.abstractSkin lesion segmentation has a critical role in the early and accurate diagnosis of skin cancer by computerized systems. However, automatic segmentation of skin lesions in dermoscopic images is a challenging task owing to difficulties including artifacts (hairs, gel bubbles, ruler markers), indistinct boundaries, low contrast and varying sizes and shapes of the lesion images. This paper proposes a novel and effective pipeline for skin lesion segmentation in dermoscopic images combining a deep convolutional neural network named as You Only Look Once (YOLO) and the GrabCut algorithm. This method performs lesion segmentation using a dermoscopic image in four steps: 1. Removal of hairs on the lesion, 2. Detection of the lesion location, 3. Segmentation of the lesion area from the background, 4. Post-processing with morphological operators. The method was evaluated on two publicly well-known datasets, that is the PH2 and the ISBI 2017 (Skin Lesion Analysis Towards Melanoma Detection Challenge Dataset). The proposed pipeline model has achieved a 90% sensitivity rate on the ISBI 2017 dataset, outperforming other deep learning-based methods. The method also obtained close results according to the results obtained from other methods in the literature in terms of metrics of accuracy, specificity, Dice coefficient, and Jaccard index.en_US
dc.description.sponsorshipResearch Fund (Scientific Research Projects Coordination Unit) of the Kirikkale University [2018/40]en_US
dc.description.sponsorshipThis paper was supported by Research Fund (Scientific Research Projects Coordination Unit) of the Kirikkale University. Project Number: 2018/40.en_US
dc.identifier.citationÜnver HM, Ayan E. Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm. Diagnostics. 2019; 9(3):72.en_US
dc.identifier.doi10.3390/diagnostics9030072
dc.identifier.issn2075-4418
dc.identifier.issue3en_US
dc.identifier.pmid31295856
dc.identifier.scopus2-s2.0-85072617653
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/diagnostics9030072
dc.identifier.urihttps://hdl.handle.net/20.500.12587/7692
dc.identifier.volume9en_US
dc.identifier.wosWOS:000487983100027
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpien_US
dc.relation.ispartofDiagnostics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectskin canceren_US
dc.subjectskin lesion segmentationen_US
dc.subjectmelanomaen_US
dc.subjectconvolutional neural networksen_US
dc.subjectYoloen_US
dc.subjectGrabCuten_US
dc.titleSkin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithmen_US
dc.typeArticle

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