ArticlePloS one2023
Enhancement of VGG16 model with multi-view and spatial dropout for classification of mosquito vectors.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed, 11 citations in OpenAlex.
- Malaria Parasite Cell Classification Using Transfer Learning with State-of-the-Art CNN Architectures.Biology · 2025Article
- AI-driven convolutional neural networks for accurate identification of yellow fever vectors.Parasites & vectors · 2024Article
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Authors and funding
6 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mosquitoes transmit pathogens that can cause numerous significant infectious diseases in humans and animals such as malaria, dengue fever, chikungunya fever, and encephalitis. Although the VGG16 model is not one of the most advanced CNN networks, it is reported that a fine-tuned VGG16 model achieves accuracy over 90% when applied to the classification of mosquitoes. The present study sets out to improve the accuracy and robustness of the VGG16 network by incorporating spatial dropout layers to regularize the network and by modifying its structure to incorporate multi-view inputs. Herein, four models are implemented: (A) early-combined, (B) middle-combined, (C) late-combined, and (D) ensemble model. Moreover, a structure for combining Models (A), (B), (C), and (D), known as the classifier, is developed. Two image datasets, including a reference dataset of mosquitoes in South Korea and a newly generated dataset of mosquitoes in Thailand, are used to evaluate our models. Regards the reference dataset, the average accuracy of ten runs improved from 83.26% to 99.77%, while the standard deviation decreased from 2.60% to 0.12%. When tested on the new dataset, the classifier's accuracy was also over 99% with a standard deviation of less than 2%. This indicates that the algorithm achieves high accuracy with low variation and is independent of a particular dataset. To evaluate the robustness of the classifier, it was applied to a small dataset consisting of mosquito images captured under various conditions. Its accuracy dropped to 86.14%, but after retraining with the small dataset, it regained its previous level of precision. This demonstrates that the classifier is resilient to variation in the dataset and can be retrained to adapt to the variation. The classifier and the new mosquito dataset could be utilized to develop an application for efficient and rapid entomological surveillance for the prevention and control of mosquito-borne diseases.
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Registered trials
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