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|a 10.1007/s11227-023-05288-y
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|a eng
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| 100 |
1 |
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|a Gupta, Aditya
|e verfasserin
|4 aut
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| 245 |
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|a Blockchain-enabled healthcare monitoring system for early Monkeypox detection
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|c 2023
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| 336 |
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 28.09.2023
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|a published: Print-Electronic
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|a Citation Status Publisher
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| 520 |
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|a © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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|a The recent emergence of monkeypox poses a life-threatening challenge to humans and has become one of the global health concerns after COVID-19. Currently, machine learning-based smart healthcare monitoring systems have demonstrated significant potential in image-based diagnosis including brain tumor identification and lung cancer diagnosis. In a similar fashion, the applications of machine learning can be utilized for the early identification of monkeypox cases. However, sharing critical health information with various actors such as patients, doctors, and other healthcare professionals in a secure manner remains a research challenge. Motivated by this fact, our paper presents a blockchain-enabled conceptual framework for the early detection and classification of monkeypox using transfer learning. The proposed framework is experimentally demonstrated in Python 3.9 using a monkeypox dataset of 1905 images obtained from the GitHub repository. To validate the effectiveness of the proposed model, various performance estimators, namely accuracy, recall, precision, and F1-score, are employed. The performance of different transfer learning models, namely Xception, VGG19, and VGG16, is compared against the presented methodology. Based on the comparison, it is evident that the proposed methodology effectively detects and classifies the monkeypox disease with a classification accuracy of 98.80%. In future, multiple skin diseases such as measles and chickenpox can be diagnosed using the proposed model on the skin lesion datasets
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|a Journal Article
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|a Blockchain
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| 650 |
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|a IPFS
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| 650 |
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|a Machine learning
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| 650 |
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4 |
|a Monkeypox
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| 650 |
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4 |
|a Transfer learning
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| 700 |
1 |
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|a Bhagat, Monu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Jain, Vibha
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t The Journal of supercomputing
|d 1998
|g (2023) vom: 20. Apr., Seite 1-25
|w (DE-627)NLM098252410
|x 0920-8542
|7 nnas
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|g year:2023
|g day:20
|g month:04
|g pages:1-25
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|u http://dx.doi.org/10.1007/s11227-023-05288-y
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