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dc.contributor.authorBibi, Sobia
dc.contributor.authorKhan, Muhammad Attique
dc.contributor.authorShah, Jamal Hussain
dc.contributor.authorDamaševičius, Robertas
dc.contributor.authorAlasiry, Areej
dc.contributor.authorMarzougui, Mehrez
dc.contributor.authorAlhaisoni, Majed
dc.contributor.authorMasood, Anum
dc.date.accessioned2024-03-18T12:21:10Z
dc.date.available2024-03-18T12:21:10Z
dc.date.created2023-10-31T09:34:12Z
dc.date.issued2023
dc.identifier.citationDiagnostics (Basel). 2023, 13 (19), .en_US
dc.identifier.issn2075-4418
dc.identifier.urihttps://hdl.handle.net/11250/3122872
dc.description.abstractCancer is one of the leading significant causes of illness and chronic disease worldwide. Skin cancer, particularly melanoma, is becoming a severe health problem due to its rising prevalence. The considerable death rate linked with melanoma requires early detection to receive immediate and successful treatment. Lesion detection and classification are more challenging due to many forms of artifacts such as hairs, noise, and irregularity of lesion shape, color, irrelevant features, and textures. In this work, we proposed a deep-learning architecture for classifying multiclass skin cancer and melanoma detection. The proposed architecture consists of four core steps: image preprocessing, feature extraction and fusion, feature selection, and classification. A novel contrast enhancement technique is proposed based on the image luminance information. After that, two pre-trained deep models, DarkNet-53 and DensNet-201, are modified in terms of a residual block at the end and trained through transfer learning. In the learning process, the Genetic algorithm is applied to select hyperparameters. The resultant features are fused using a two-step approach named serial-harmonic mean. This step increases the accuracy of the correct classification, but some irrelevant information is also observed. Therefore, an algorithm is developed to select the best features called marine predator optimization (MPA) controlled Reyni Entropy. The selected features are finally classified using machine learning classifiers for the final classification. Two datasets, ISIC2018 and ISIC2019, have been selected for the experimental process. On these datasets, the obtained maximum accuracy of 85.4% and 98.80%, respectively. To prove the effectiveness of the proposed methods, a detailed comparison is conducted with several recent techniques and shows the proposed framework outperforms.en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleMSRNet: Multiclass Skin Lesion Recognition Using Additional Residual Block Based Fine-Tuned Deep Models Information Fusion and Best Feature Selectionen_US
dc.title.alternativeMSRNet: Multiclass Skin Lesion Recognition Using Additional Residual Block Based Fine-Tuned Deep Models Information Fusion and Best Feature Selectionen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber0en_US
dc.source.volume13en_US
dc.source.journalDiagnostics (Basel)en_US
dc.source.issue19en_US
dc.identifier.doi10.3390/diagnostics13193063
dc.identifier.cristin2190303
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal