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Performance of majority voting transfer learning deep learning monkeypox disease detection
IAES International Journal of Artificial Intelligence
ISSN 2252-8938, 2089-4872
Terbit 2026-08-01
Penerbit Institute of Advanced Engineering and Science
Bahasa eng
Abstrak
Global health concerns have been raised by the advent of monkeypox following the COVID-19 epidemic, highlighting the need for reliable automated systems to support early skin disease screening. This study proposes a monkeypox skin disease classification framework using a majority voting ensemble based on transfer learning. The ensemble combines predictions from multiple pretrained convolutional neural networks (CNNs) to improve classification robustness. A publicly accessible dataset comprising four classes: monkeypox, chickenpox, measles, and normal skin was used for the experiments. The results indicate that while a single ResNet50 model achieved the highest overall accuracy (99.15%), the majority voting approach yielded higher precision than several individual models, demonstrating improved reliability in distinguishing monkeypox cases. These findings suggest that ensemble-based majority voting can enhance the robustness of monkeypox skin disease classification and may support computer-aided screening systems.
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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc)
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
<dc:title xml:lang="en-US">Performance of majority voting transfer learning deep learning monkeypox disease detection</dc:title>
<dc:creator>Nafiiyah, Nur</dc:creator>
<dc:creator>Huda, Muhammad Nurul</dc:creator>
<dc:subject xml:lang="en-US"></dc:subject>
<dc:subject xml:lang="en-US">Deep learning; Majority voting; Monkeypox; ResNet50; Transfer learning</dc:subject>
<dc:description xml:lang="en-US">Global health concerns have been raised by the advent of monkeypox following the COVID-19 epidemic, highlighting the need for reliable automated systems to support early skin disease screening. This study proposes a monkeypox skin disease classification framework using a majority voting ensemble based on transfer learning. The ensemble combines predictions from multiple pretrained convolutional neural networks (CNNs) to improve classification robustness. A publicly accessible dataset comprising four classes: monkeypox, chickenpox, measles, and normal skin was used for the experiments. The results indicate that while a single ResNet50 model achieved the highest overall accuracy (99.15%), the majority voting approach yielded higher precision than several individual models, demonstrating improved reliability in distinguishing monkeypox cases. These findings suggest that ensemble-based majority voting can enhance the robustness of monkeypox skin disease classification and may support computer-aided screening systems.</dc:description>
<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
<dc:contributor xml:lang="en-US"></dc:contributor>
<dc:date>2026-08-01</dc:date>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
<dc:type xml:lang="en-US"></dc:type>
<dc:format>application/pdf</dc:format>
<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/30502</dc:identifier>
<dc:identifier>10.11591/ijai.v15.i4.pp3782-3791</dc:identifier>
<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 15, No 4: August 2026; 3782-3791</dc:source>
<dc:source>2252-8938</dc:source>
<dc:source>2089-4872</dc:source>
<dc:source>10.11591/ijai.v15.i4</dc:source>
<dc:language>eng</dc:language>
<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/30502/15341</dc:relation>
<dc:rights xml:lang="en-US">Copyright (c) 2026 Nur Nafiiyah, Muhammad Nurul Huda</dc:rights>
<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>
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Nafiiyah, N., & Huda, M. N. (2026). Performance of majority voting transfer learning deep learning monkeypox disease detection. IAES International Journal of Artificial Intelligence, 15(4), 3782–3791. https://doi.org/10.11591/ijai.v15.i4.pp3782-3791
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IEEE
N. Nafiiyah and M. N. Huda, “Performance of majority voting transfer learning deep learning monkeypox disease detection,” IAES International Journal of Artificial Intelligence, vol. 15, no. 4, pp. 3782–3791, 2026, doi: 10.11591/ijai.v15.i4.pp3782-3791.
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