ArticleFrontiers in microbiology2025
Can deep learning technology really recognize Mpox? A positive response from the EfficientNet model.
Article in Frontiers in microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- A customized MobileNetV2-based lightweight CNN for monkeypox detection and classification.Scientific reports · 2026Article
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Authors and funding
3 authors.
Funding
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Abstract
On July 23, 2022, the World Health Organization (WHO) officially declared the Mpox outbreak a "Public Health Emergency of International Concern" (PHEIC), highlighting the urgent need for effective prevention and control measures worldwide. To assist healthcare managers and medical professionals in efficiently and accurately identifying Mpox cases from similar conditions, this study proposes a lightweight deep learning model. The model uses EfficientNet as the backbone network and employs transfer learning techniques to transfer the pre-trained EfficientNet parameters, originally trained on the ImageNet dataset, into this model. This approach allows the model to have strong generalization capabilities while controlling the number of parameters and computational complexity. Experimental results show that, compared to existing advanced methods, the proposed method not only has a lower number of parameters (only 4.14 M), but also achieves optimal values in most performance metrics, including
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.