Skin lesion segmentation method for dermoscopy images using artificial bee colony algorithm

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dc.contributor.authorAljanabi, Mohanad
dc.contributor.authorÖzok, Yasa Ekşioğlu
dc.contributor.authorRahebi, Javad
dc.contributor.authorAbdullah, Ahmad S.
dc.date.accessioned2021-05-15T12:41:41Z
dc.date.available2021-05-15T12:41:41Z
dc.date.issued2018
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Elektrik - Elektronik Mühendisliği Bölümüen_US
dc.descriptionABDULLAH, AHMED SULAIMAN/0000-0002-8578-1760; aljanabi, mohanad/0000-0002-6551-2379
dc.description.abstractThe occurrence rates of melanoma are rising rapidly, which are resulting in higher death rates. However, if the melanoma is diagnosed in Phase I, the survival rates increase. The segmentation of the melanoma is one of the largest tasks to undertake and achieve when considering both beneath and over the segmentation. In this work, a new approach based on the artificial bee colony (ABC) algorithm is proposed for the detection of melanoma from digital images. This method is simple, fast, flexible, and requires fewer parameters compared with other algorithms. The proposed approach is applied on the PH2, ISBI 2016 challenge, the ISBI 2017 challenge, and Dermis datasets. These bases contained images are affected by different abnormalities. The formation of the databases consists of images collected from different sources; they are bases with different types of resolution, lighting, etc., so in the first step, the noise was removed from the images by using morphological filtering. In the next step, the ABC algorithm is used to find the optimum threshold value for the melanoma detection. The proposed approach achieved good results in the conditions of high specificity. The experimental results suggest that the proposed method accomplished higher performance compared to the ground truth images supported by a Dermatologist. For the melanoma detection, the method achieved an average accuracy and Jaccard's coefficient in the range of 95.24-97.61%, and 83.56-85.25% in these four databases. To show the robustness of this work, the results were compared to existing methods in the literature for melanoma detection. High values for estimation performance confirmed that the proposed melanoma detection is better than other algorithms, which demonstrates the highly differential power of the newly introduced features.en_US
dc.identifier.doi10.3390/sym10080347
dc.identifier.issn2073-8994
dc.identifier.issue8en_US
dc.identifier.scopus2-s2.0-85052491100
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/sym10080347
dc.identifier.urihttps://hdl.handle.net/20.500.12939/840
dc.identifier.volume10en_US
dc.identifier.wosWOS:000442486600049
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorÖzok, Yasa Ekşioğlu
dc.language.isoen
dc.publisherMdpien_US
dc.relation.ispartofSymmetry-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial Bee Colony (ABC)en_US
dc.subjectImage Segmentationen_US
dc.subjectSkin Melanomaen_US
dc.subjectHeuristic Methoden_US
dc.subjectDermoscopyen_US
dc.titleSkin lesion segmentation method for dermoscopy images using artificial bee colony algorithm
dc.typeArticle

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