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Computer-aided detection and classification of monkeypox and chickenpox lesion in human subjects using deep learning framework

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorUzun Ozsahin, Dilber
dc.contributor.authorMustapha, Mubarak Taiwo
dc.contributor.authorUzun, Berna
dc.contributor.authorDuwa, Basil
dc.contributor.authorOzsahin, Ilker
dc.date.accessioned2023-10-04T14:02:25Z
dc.date.available2023-10-04T14:02:25Z
dc.date.issued2023-01-12
dc.description.abstractMonkeypox is a zoonotic viral disease caused by the monkeypox virus. After its recent outbreak, it has become clear that a rapid, accurate, and reliable diagnosis may help reduce the risk of a future outbreak. The presence of skin lesions is one of the most prominent symptoms of the disease. However, this symptom is also peculiar to chickenpox. The resemblance in skin lesions in the human subject may disrupt effective diagnosis and, as a result, lead to misdiagnosis. Such misdiagnosis can lead to the further spread of the disease as it is a communicable disease and can eventually result in an outbreak. As deep learning (DL) algorithms have recently been regarded as a promising technique in medical fields, we have been attempting to integrate a well-trained DL algorithm to assist in the early detection and classification of skin lesions in human subjects. This study used two open-sourced digital skin images for monkeypox and chickenpox. A two-dimensional convolutional neural network (CNN) consisting of four convolutional layers was applied. Afterward, three MaxPooling layers were used after the second, third, and fourth convolutional layers. Finally, we evaluated the performance of our proposed model with state of the art deep-learning models for skin lesions detection. Our proposed CNN model outperformed all DL models with a test accuracy of 99.60%. In addition, a weighted average precision, recall, F1 score of 99.00% was recorded. Subsequently, Alex Net outperformed other pre-trained models with an accuracy of 98.00%. The VGGNet consisting of VGG16 and VGG19 performed least well with an accuracy of 80.00%. Due to the uniqueness of the proposed model and image augmentation techniques applied, the proposed CNN model is generalized and avoids over-fitting. This model would be helpful for the rapid and accurate detection of monkeypox using digital skin images of patients with suspected monkeypox.en
dc.identifier.bibliographicCitationUzun Ozsahin D, Mustapha MT, Uzun B, Duwa B, Ozsahin I. (2023). Computer-Aided Detection and Classification of Monkeypox and Chickenpox Lesion in Human Subjects Using Deep Learning Framework. Diagnostics, 13(2):292.en
dc.identifier.doihttps://doi.org/10.3390/diagnostics13020292
dc.identifier.issn2075-4418
dc.identifier.publicationfirstpage1es
dc.identifier.publicationissue2es
dc.identifier.publicationlastpage14es
dc.identifier.publicationtitleDiagnosticses
dc.identifier.publicationvolume13es
dc.identifier.urihttps://hdl.handle.net/10016/38531
dc.identifier.uxxiAR/0000033041
dc.language.isoenges
dc.publisherMDPIes
dc.rights© The author(s)es
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accesses
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaMedicinaes
dc.subject.otherDeep learning algorithmen
dc.subject.otherDiagnosisen
dc.subject.otherChickenpoxen
dc.subject.otherLesionen
dc.subject.otherOutbreaken
dc.subject.otherMonkeypoxen
dc.titleComputer-aided detection and classification of monkeypox and chickenpox lesion in human subjects using deep learning frameworken
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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