Wavelet transform based deep residual neural network and ReLU based Extreme Learning Machine for skin lesion classification

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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.accessioned2023-08-10T07:52:26Z
dc.date.available2023-08-10T07:52:26Z
dc.date.issued2023en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionDeanship of Scientific Research at Jouf University;en_US
dc.description.abstractSkin cancer is one of the most widespread threats to human health worldwide. Therefore, early-stage recognition and detection of these diseases are crucial for patients' lives. Computer-aided methods can be used to solve this problem with high performance. We presented a wavelet transform-based deep residual neural network (WT-DRNNet) for skin lesion classification. In the proposed model, wavelet transformation, pooling, and normali-zation reveal more refined details and eliminate unwanted details from skin lesion images. Then, deep features are extracted with the residual neural network based on transfer learning as a feature extractor. Finally, these deep features were combined with the global average pooling approach, and the training phase was carried out using the Extreme Learning Machine based on the ReLu activation function. The ISIC2017 and HAM10000 datasets were used in the experimental works to test the proposed model's performance. The performance metrics of accuracy, specificity, precision, and F1-Score of the proposed model for the ISIC2017 dataset were 96.91%, 97.68%, 96.43%, and 95.79%, respectively, while these metrics for the HAM10000 dataset were 95.73%, 98.8%, 95.84%, and 93.44%, respectively. These results outperform the state-of-the-art to classify skin lesions. As a result, the proposed model can assist specialist physicians in automatically classifying cancer-based on skin lesion imagesen_US
dc.description.sponsorshipDeanship of Scientific Research at Jouf University; [DSR2022-RG-0112]en_US
dc.identifier.citationAlenezi, F., Armghan, A., & Polat, K. (2023). Wavelet transform based deep residual neural network and ReLU based Extreme Learning Machine for skin lesion classification. Expert Systems with Applications, 213, 119064.en_US
dc.identifier.doi10.1016/j.eswa.2022.119064
dc.identifier.endpage8en_US
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85140445498en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.eswa.2022.119064
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11475
dc.identifier.volume213en_US
dc.identifier.wosWOS:000885319700003en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems with Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep Learningen_US
dc.subjectResidual Neural Networken_US
dc.subjectSkin Lesion Classificationen_US
dc.subjectWavelet Transformen_US
dc.subjectISIC2017en_US
dc.subjectHAM10000en_US
dc.titleWavelet transform based deep residual neural network and ReLU based Extreme Learning Machine for skin lesion classificationen_US
dc.typeArticleen_US

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