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.accessioned2025-02-22T14:13:31Z
dc.date.available2025-02-22T14:13:31Z
dc.date.issued2024
dc.departmentDicle Üniversitesien_US
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.en_US
dc.identifier.doi10.36306/konjes.1471289
dc.identifier.endpage837en_US
dc.identifier.issn2667-8055
dc.identifier.issue4en_US
dc.identifier.startpage822en_US
dc.identifier.trdizinid1283656en_US
dc.identifier.urihttps://doi.org/10.36306/konjes.1471289
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1283656
dc.identifier.urihttps://hdl.handle.net/11468/30048
dc.identifier.volume12en_US
dc.indekslendigikaynakTR-Dizin
dc.language.isoenen_US
dc.relation.ispartofKonya mühendislik bilimleri dergisi (Online)en_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_TR_20250222
dc.subjectClassificationen_US
dc.subjectDeep Learningen_US
dc.subjectMonkeypox Diseaseen_US
dc.subjectAdaptive Ensemble Learningen_US
dc.titleDEEP LEARNING-BASED ADAPTIVE ENSEMBLE LEARNING MODEL FOR CLASSIFICATION OF MONKEYPOX DISEASEen_US
dc.typeArticleen_US

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