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This study introduces a hybrid framework that combines the YOLO 11M model for mosquito identification, a Vision Transformer (ViT) for visual feature extraction, and a convolutional Gated Recurrent Unit (ConvGRU) for classification to detect dengue virus-infected mosquitoes. By addressing the challenges posed by small sizes and complex backgrounds in video data, the framework significantly improves feature extraction and classification accuracy. The ConvGRU model outperformed traditional methods, achieving 88.88% accuracy and demonstrating the effectiveness of integrating convolutional and sequence-based approaches for analyzing mosquito locomotion behavior.
Achieving nearly 89% accuracy in detecting dengue-infected mosquitoes, this framework redefines feature extraction in complex video environments.
Identifying dengue virus-infected mosquitoes from control mosquitoes is a major challenge in analyzing mosquito locomotion behavior due to the small size and complexity of the video background. Conventional AI methods are often unable to extract accurate features from video frames and produce erroneous features. In this study, a three-step framework is introduced: first, mosquitoes are identified and the background is removed using the YOLO 11M model, then visual features are extracted using the Vision Transformer (ViT), and finally the videos are classified with a convolutional GRU (ConvGRU) classifier. A comparative analysis of different models, including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and their convolutional versions showed that the ConvGRU model achieved the best performance; it achieved 88.88% accuracy, 84.45% precision, 82.82% recall, and 82.81% F1 score. These results demonstrate that combining convolutional models with sequence-based networks, especially in the ConvGRU model, allows the simultaneous extraction of precise spatial features and long-term temporal dependencies from mosquito movements. Finally, the proposed framework provides a reliable solution for analyzing mosquito behavior in complex environments.
