An Effective Combination of Deep and Machine Learning Models for Monkeypox Detection

Authors

Partho GhoseM. BiswasSohel Ahmed Joni Rabiul Rahat Nishat Tasnin

Abstract

This paper presents a comparative study of hybrid deep learning models for detecting Monkeypox from dermatographic images. We implemented transfer learning with ResNet-50, VGG-16, and MobileNet architectures for high-accuracy disease classification.

Keywords

Medical AIComputer VisionDeep LearningTransfer Learning

Open at publisher

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-981-97-3937-0_3 © The Author(s), under exclusive licence to Springer Nature. Personal use of this material is permitted. Permission from Springer Nature must be obtained for all other uses.

Key Contributions

•Comparative study of hybrid deep learning models to accurately detect Monkeypox from dermatographic images.
•Published in Springer (2024)
•DOI: 10.1007/978-981-97-3937-0_3

Citation

Partho Ghose, M. Biswas, Sohel Ahmed Joni, Rabiul Rahat, Nishat Tasnin. "An Effective Combination of Deep and Machine Learning Models for Monkeypox Detection." Springer, 2024. doi: 10.1007/978-981-97-3937-0_3.

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