A novel multi-task learning network based on melanoma segmentation and classification with skin lesion images

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dc.authorid0000-0002-4099-1254en_US
dc.authorid0000-0002-9062-7493en_US
dc.authorid0000-0003-1840-9958en_US
dc.contributor.authorAlenezi, Fayadh
dc.contributor.authorArmghan, Ammar
dc.contributor.authorPolat, Kemal
dc.date.accessioned2024-02-06T07:17:31Z
dc.date.available2024-02-06T07:17:31Z
dc.date.issued2023en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionThis work was funded by the Deanship of Scientific Research at Jouf University under Grant Number (DSR2022-RG-0112).en_US
dc.description.abstractMelanoma is known worldwide as a malignant tumor and the fastest-growing skin cancer type. It is a very life-threatening disease with a high mortality rate. Automatic melanoma detection improves the early detection of the disease and the survival rate. In accordance with this purpose, we presented a multi-task learning approach based on melanoma recognition with dermoscopy images. Firstly, an effective pre-processing approach based on max pooling, contrast, and shape filters is used to eliminate hair details and to perform image enhancement operations. Next, the lesion region was segmented with a VGGNet model-based FCN Layer architecture using enhanced images. Later, a cropping process was performed for the detected lesions. Then, the cropped images were converted to the input size of the classifier model using the very deep super-resolution neural network approach, and the decrease in image resolution was minimized. Finally, a deep learning network approach based on pre-trained convolutional neural networks was developed for melanoma classification. We used the International Skin Imaging Collaboration, a publicly available dermoscopic skin lesion dataset in experimental studies. While the performance measures of accuracy, specificity, precision, and sensitivity, obtained for segmentation of the lesion region, were produced at rates of 96.99%, 92.53%, 97.65%, and 98.41%, respectively, the performance measures achieved rates for classification of 97.73%, 99.83%, 99.83%, and 95.67%, respectively.en_US
dc.description.sponsorshipDeanship of Scientific Research at Jouf University [DSR2022-RG-0112]en_US
dc.identifier.citationAlenezi, F., Armghan, A., & Polat, K. (2023). A Novel Multi-Task Learning Network Based on Melanoma Segmentation and Classification with Skin Lesion Images. Diagnostics, 13(2), 262.en_US
dc.identifier.doi10.3390/diagnostics13020262
dc.identifier.endpage15en_US
dc.identifier.issn2075-4418
dc.identifier.issue2en_US
dc.identifier.pmid36673072en_US
dc.identifier.scopus2-s2.0-85146748385en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.3390/diagnostics13020262
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11979
dc.identifier.volume13en_US
dc.identifier.wosWOS:000914413200001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.ispartofDiagnosticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMelanoma Classification and Segmentationen_US
dc.subjectDeep Learningen_US
dc.subjectSuper-Resolutionen_US
dc.subjectMulti-Task Learning Networken_US
dc.subjectDermoscopic Imagesen_US
dc.subjectNeural-Networken_US
dc.titleA novel multi-task learning network based on melanoma segmentation and classification with skin lesion imagesen_US
dc.typeArticleen_US

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