A novel approach to skin lesion segmentation: Multipath fusion model with fusion loss

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dc.authorid0000-0002-7201-6963en_US
dc.authorid0000-0002-8061-8059en_US
dc.authorid0000-0002-6357-0073en_US
dc.authorid0000-0001-8461-1404en_US
dc.authorid0000-0003-1840-9958en_US
dc.contributor.authorAlhudhaif, Adi
dc.contributor.authorÖcal, Hakan
dc.contributor.authorBarışçı, Necaattin
dc.contributor.authorAtacak, İsmail
dc.contributor.authorNour, Majid
dc.contributor.authorPolat, Kemal
dc.date.accessioned2023-08-09T08:12:34Z
dc.date.available2023-08-09T08:12:34Z
dc.date.issued2022en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractSegmentation of skin lesions plays a very important role in the early detection of skin cancer. However, indistinguishability due to various artifacts such as hair and contrast between normal skin and lesioned skin is an important challenge for specialist dermatologists. Computer-aided diagnostic systems using deep convolutional neural networks are gaining importance in order to cope with difficulties. This study focuses on deep learning-based fusion networks and fusion loss functions. For the automatic segmentation of skin lesions, U-Net (U-Net + ResNet 2D) with 2D residual blocks and 2D volumetric convolutional neural networks were fused for the first time in this study. Also, a new fusion loss function is proposed by combining Dice Loss (DL) and Focal Tversky Loss (FTL) to make the proposed fused model more robust. Of the 2594 image dataset, 20% is reserved for test data and 80% for training data. In test data training, a Jaccard score of 0.837 and a dice score of 0.918 were obtained. The proposed model was also scored on the ISIC 2018 Task 1 test images, whose ground truths were not shared. The proposed model performed well and achieved a Jaccard index of 0.800 and a dice score of 0.880 in the ISIC 2018 Task 1 test set. In addition, it has been observed that the new fused loss function obtained by fusing Focal Tversky Loss and Dice Loss functions in the proposed model increases the robustness of the model in the tests. The proposed new loss function fusion model has outstripped the cutting-edge approaches in the literature.en_US
dc.identifier.citationAlhudhaif, A., Ocal, H., Barisci, N., Atacak, İ., Nour, M., & Polat, K. (2022). A novel approach to skin lesion segmentation: Multipath fusion model with fusion loss. Computational and Mathematical Methods in Medicine, 2022.en_US
dc.identifier.doi10.1155/2022/2157322
dc.identifier.endpage12en_US
dc.identifier.issn1748-670X
dc.identifier.issn1748-6718
dc.identifier.pmid35936380en_US
dc.identifier.scopus2-s2.0-85135551597en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.1155/2022/2157322
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11464
dc.identifier.volume2022en_US
dc.identifier.wosWOS:000880655700002en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherHindawi Ltden_US
dc.relation.ispartofComputational and Mathematical Methods in Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCanceren_US
dc.subjectMelanomaen_US
dc.subjectUSen_US
dc.titleA novel approach to skin lesion segmentation: Multipath fusion model with fusion lossen_US
dc.typeArticleen_US

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