Enhancing Monkeypox Detection: A Machine Learning Approach to Symptom Analysis and Disease Prediction
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-63211-2_5
Reference22 articles.
1. Ejaz, M., Jabeen, M., Sharif, M., Syed, M.A., Shah, P.T., Faryal, R.: Human monkeypox: an updated appraisal on epidemiology, evolution, pathogenesis, clinical manifestations, and treatment strategies. J. Basic Microbiol. 64(2), e2300455 (2024). https://doi.org/10.1002/jobm.202300455
2. Zhang, S., Li, Y.D., Cai, Y.R., et al.: Compositional features analysis by machine learning in genome represents linear adaptation of monkeypox virus. Front. Genet. 15 (2024). https://doi.org/10.3389/fgene.2024.1361952
3. Van Dijck, C., Hoff, N.A., Mbala-Kingebeni, P., et al.: Emergence of mpox in the post-smallpox era-a narrative review on mpox epidemiology. Clin. Microbiol. Infect. 29(12), 1487–1492 (2023). https://doi.org/10.1016/j.cmi.2023.08.008
4. Nuzzo, J.B., Borio, L.L., Gostin, L.O.: The WHO declaration of monkeypox as a global public health emergency. JAMA 328(7), 615–617 (2022). https://doi.org/10.1001/jama.2022.12513
5. Almufareh, M.F., Tehsin, S., Humayun, M., Kausar, S.A.: A transfer learning approach for clinical detection support of Monkeypox skin lesions. Diagnostics (Basel) 13(8), 1503 (2023). https://doi.org/10.3390/diagnostics13081503
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