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Deep Learning-Based Classification of Anopheles stephensi Adult Mosquitoes with Enhanced Solutions for Data Imbalance
Summary
This study uses AI and citizen science to detect the malaria-carrying mosquito Anopheles stephensi, even with imbalanced data. EfficientNet-B1 with focal loss achieved the best results for accurate mosquito surveillance.
Area of Science:
- Entomology
- Artificial Intelligence
- Public Health
Background:
- Mosquito surveillance is crucial for controlling vector-borne diseases like malaria.
- Existing AI research often uses balanced datasets, which are not representative of real-world scenarios.
- Anopheles stephensi is an invasive mosquito species and a critical vector of malaria in Asia and Africa.
Purpose of the Study:
- To develop and evaluate AI models for detecting Anopheles stephensi using citizen science data.
- To address the challenge of class imbalance in mosquito image datasets.
- To identify the most effective AI techniques for identifying Anopheles stephensi in diverse natural environments.
Main Methods:
- Utilized a dataset of 1195 Anopheles stephensi images and 6021 non-Anopheles stephensi images captured by smartphones.
- Experimented with various state-of-the-art AI models and techniques to handle class imbalance.
- Employed focal loss and evaluated the impact of data downsampling on model performance.
- Used Grad-CAM for visualizing model feature learning.
Main Results:
- The EfficientNet-B1 architecture demonstrated superior performance in detecting Anopheles stephensi.
- Downsampling the majority class led to significantly poor classification accuracies.
- Optimal classification results were achieved using the original, non-downsampled dataset combined with focal loss.
- Grad-CAM visualizations confirmed that the AI model learned relevant anatomical features for species identification.
Conclusions:
- AI integration with citizen science is effective for mosquito surveillance, particularly for challenging species like Anopheles stephensi.
- Focal loss and using the original imbalanced dataset outperform data downsampling methods for detecting Anopheles stephensi.
- The study highlights the potential of AI in improving malaria vector control strategies through enhanced mosquito detection.
