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Journals / Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi / 2026 / Cilt: 13 Sayı: 1

Detection of Monkeypox Among Different Pox Diseases with Different Pre-Trained Deep Learning Models

Muhammed Çelik(TOKAT GAZİOSMANPAŞA ÜNİVERSİTESİ)0000-0001-6909-7830
Özkan İnik(TOKAT GAZİOSMANPAŞA ÜNİVERSİTESİ)0000-0003-4728-8438
Pages
10–21
Publication Date
2023-03-01
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

When the amount of data increased, the success of deep networks was better because the weights of the deep models were updated at the desired level.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Monkeypox is a viral disease that has recently rapidly spread.
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Key Findings & Data:Therefore, in this study, monkeypox disease was detected in cowpox, smallpox, and chickenpox diseases using the pre-trained deep learning models VGG-19, VGG-16, MobileNet V2, GoogLeNet, and EfficientNet-B0.
💡
Conclusion & Impact:When the amount of data increased, the success of deep networks was better because the weights of the deep models were updated at the desired level.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:VggNet · GoogLeNet · MobileNet

Abstract

Monkeypox is a viral disease that has recently rapidly spread. Experts have trouble diagnosing the disease because it is similar to other smallpox diseases. For this reason, researchers are working on artificial intelligence-based computer vision systems for the diagnosis of monkeypox to make it easier for experts, but a professional dataset has not yet been created. Instead, studies have been carried out on datasets obtained by collecting informal images from the Internet. The accuracy of state-of-the-art deep learning models on these datasets is unknown. Therefore, in this study, monkeypox disease was detected in cowpox, smallpox, and chickenpox diseases using the pre-trained deep learning models VGG-19, VGG-16, MobileNet V2, GoogLeNet, and EfficientNet-B0. In experimental studies on the original and augmented datasets, MobileNet V2 achieved the highest classification accuracy of 99.25% on the augmented dataset. In contrast, the VGG-19 model achieved the highest classification accuracy with 78.82% of the original data. Considering these results, the shallow model yielded better results for the datasets with fewer images. When the amount of data increased, the success of deep networks was better because the weights of the deep models were updated at the desired level.

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Cite This Article

Çelik, M., İnik, Ö. (2023). Detection of Monkeypox Among Different Pox Diseases with Different Pre-Trained Deep Learning Models. Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt: 13 Sayı: 1, 10–21. https://doi.org/10.21597/jist.1206453

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