Artikel ; Online: Monkeypox Virus Detection Using Pre-trained Deep Learning-based Approaches.
2022 Band 46, Heft 11, Seite(n) 78
Abstract: Monkeypox virus is emerging slowly with the decline of COVID-19 virus infections around the world. People are afraid of it, thinking that it would appear as a pandemic like COVID-19. As such, it is crucial to detect them earlier before widespread ... ...
Abstract | Monkeypox virus is emerging slowly with the decline of COVID-19 virus infections around the world. People are afraid of it, thinking that it would appear as a pandemic like COVID-19. As such, it is crucial to detect them earlier before widespread community transmission. AI-based detection could help identify them at the early stage. In this paper, we aim to compare 13 different pre-trained deep learning (DL) models for the Monkeypox virus detection. For this, we initially fine-tune them with the addition of universal custom layers for all of them and analyse the results using four well-established measures: Precision, Recall, F1-score, and Accuracy. After the identification of the best-performing DL models, we ensemble them to improve the overall performance using a majority voting over the probabilistic outputs obtained from them. We perform our experiments on a publicly available dataset, which results in average Precision, Recall, F1-score, and Accuracy of 85.44%, 85.47%, 85.40%, and 87.13%, respectively with the help of our proposed ensemble approach. These encouraging results, which outperform the state-of-the-art methods, suggest that the proposed approach is applicable to health practitioners for mass screening. |
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Mesh-Begriff(e) | COVID-19/diagnosis ; Deep Learning ; Humans ; Monkeypox virus ; Pandemics |
Sprache | Englisch |
Erscheinungsdatum | 2022-10-06 |
Erscheinungsland | United States |
Dokumenttyp | Journal Article |
ZDB-ID | 423488-1 |
ISSN | 1573-689X ; 0148-5598 |
ISSN (online) | 1573-689X |
ISSN | 0148-5598 |
DOI | 10.1007/s10916-022-01868-2 |
Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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