| Autor: |
Gelan Ayana, Kokeb Dese, Hundessa Daba Nemomssa, Hamdia Murad, Efrem Wakjira, Gashaw Demlew, Dessalew Yohannes, Ketema Lemma Abdi, Elbetel Taye, Filimona Bisrat, Tenager Tadesse, Legesse Kidanne, Se-woon Choe, Netsanet Workneh Gidi, Bontu Habtamu, Jude Kong |
| Jazyk: |
angličtina |
| Rok vydání: |
2025 |
| Předmět: |
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| Zdroj: |
Infectious Disease Modelling, Vol 10, Iss 1, Pp 353-364 (2025) |
| Druh dokumentu: |
article |
| ISSN: |
2468-0427 |
| DOI: |
10.1016/j.idm.2024.12.002 |
| Popis: |
Acute flaccid paralysis (AFP) case surveillance is pivotal for the early detection of potential poliovirus, particularly in endemic countries such as Ethiopia. The community-based surveillance system implemented in Ethiopia has significantly improved AFP surveillance. However, challenges like delayed detection and disorganized communication persist. This work proposes a simple deep learning model for AFP surveillance, leveraging transfer learning on images collected from Ethiopia's community key informants through mobile phones. The transfer learning approach is implemented using a vision transformer model pretrained on the ImageNet dataset. The proposed model outperformed convolutional neural network-based deep learning models and vision transformer models trained from scratch, achieving superior accuracy, F1-score, precision, recall, and area under the receiver operating characteristic curve (AUC). It emerged as the optimal model, demonstrating the highest average AUC of 0.870 ± 0.01. Statistical analysis confirmed the significant superiority of the proposed model over alternative approaches (P |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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