DEEP LEARNING-BASED ADAPTIVE ENSEMBLE LEARNING MODEL FOR CLASSIFICATION OF MONKEYPOX DISEASE

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dc.contributor.authorÜzen, Hüseyin
dc.contributor.authorFırat, Hüseyin
dc.date.accessioned2025-03-08T18:29:03Z
dc.date.available2025-03-08T18:29:03Z
dc.date.issued2024
dc.departmentDicle Üniversitesi
dc.description.abstractMonkeypox a viral disease resembling smallpox often transmitted via animal contact or human-to-human transmission. Symptoms include fever, rash, and respiratory issues. Healthcare experts initially may confuse it with chickenpox or measles due to its rarity, but swollen lymph nodes typically distinguish it. Diagnosis involves tissue sampling and polymerase chain reaction (PCR) testing, although PCR tests have limitations like time consumption and false negatives. Deep learning-based detection offers advantages over PCR, including reduced risk of exposure, quicker results, and improved accuracy. In this study, a novel adaptive ensemble learning (AEL)-based model for monkeypox diagnosis is proposed. This proposed ensemble learning model aims to enhance diagnosis accuracy by combining different deep learning models, leveraging an adaptive approach for model combination. Experimental studies using MSLD and MSID datasets show promising results, with ensemble models achieving high accuracy, precision, recall, and F1 scores. The ResNet101+VGG16 (92.46% accuracy, 92.75% precision, 93.22% recall, and 92.98% F1 score) ensemble model performs best for MSLD, while DenseNet121+Xception (97.58% accuracy, 96.57% precision, 95.74% recall, and 96.14% F1 score) excels for MSID. In addition, the proposed AEL model outperforms previous studies using the same datasets, showcasing its potential for improved monkeypox diagnosis.
dc.identifier.doi10.36306/konjes.1471289
dc.identifier.endpage837
dc.identifier.issn2667-8055
dc.identifier.issue4
dc.identifier.startpage822
dc.identifier.urihttps://doi.org/10.36306/konjes.1471289
dc.identifier.urihttps://hdl.handle.net/11468/31606
dc.identifier.volume12
dc.language.isoen
dc.publisherKonya Teknik Üniversitesi
dc.relation.ispartofKonya Mühendislik Bilimleri Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_DergiPark_21250205
dc.subjectAdaptive Ensemble Learning
dc.subjectDeep Learning
dc.subjectMonkeypox Disease
dc.subjectClassification
dc.titleDEEP LEARNING-BASED ADAPTIVE ENSEMBLE LEARNING MODEL FOR CLASSIFICATION OF MONKEYPOX DISEASE
dc.typeArticle

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