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Efficient monkeypox detection using hybrid lightweight CNN architectures and optimized SVM with grid search on imbalanced data

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Abstract

The detection of the Monkeypox virus outside of Africa, with its identification in Sweden in August, followed by the World Health Organization's declaration of a global health emergency, has heightened concerns about the potential emergence of a new epidemic. Effectively managing disease detection and isolation processes is crucial to avoid the adverse impacts of a global pandemic. A hybrid classification-based approach is presented for detecting Monkeypox, focusing on the early symptoms of skin lesions, which typically appear within three days. Due to the limited availability of data, Convolutional Neural Network models were employed. The proposed method integrates multiple Convolutional Neural Network architectures, including MobileNetV3Large and EfficientNetB0, to enhance the effectiveness of feature extraction. Classification was performed using an optimized Support Vector Machine method, refined through Grid Search to ensure optimal parameter selection. The optimized Support Vector Machine highly improved the classification accuracy. This approach, aimed at generalizability, has demonstrated successful results on the Monkeypox Skin Images Dataset and Monkeypox Skin Lesion Dataset open-access datasets. The proposed method achieved a maximum accuracy of 98.67% on the Monkeypox Skin Images Dataset and 98.13% on the Monkeypox Skin Lesion Dataset when cross-validation was applied to the generated dataset partitions. This study is considered a significant step toward the early detection of Monkeypox and the development of effective intervention strategies, which will contribute to preventing future outbreaks.

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Data availability

No datasets were generated or analysed during the current study.

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Acknowledgements

The author thanks the dataset owners for sharing their datasets.

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering and Natural Sciences, University of Malatya Turgut Özal, Malatya, Turkey

    Dilber Çetintaş

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  1. Dilber Çetintaş
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D.C. was solely responsible for the conception, design, and execution of the study. All aspects of the work were performed independently by the author.

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Correspondence to Dilber Çetintaş.

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Çetintaş, D. Efficient monkeypox detection using hybrid lightweight CNN architectures and optimized SVM with grid search on imbalanced data. SIViP 19, 336 (2025). https://doi.org/10.1007/s11760-025-03915-0

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