A Hybrid Framework of Vision Transformer and Gated Recurrent Unit for Detection of Mosquito Diseases

• URL: http://arxiv.org/abs/2608.11582v1
• Date: Wed, 12 Aug 2026 02:43:47 GMT
• Title: A Hybrid Framework of Vision Transformer and Gated Recurrent Unit for Detection of Mosquito Diseases
• Abstract summary: Identifying dengue virus-infected mosquitoes from control mosquitoes is a major challenge due to the small size and complexity of the video background.<n>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)<n>The ConvGRU model achieved the best performance; it achieved 88.88% accuracy, 84.45% precision, 82.82% recall, and 82.81% F1 score.
• Score: 1.704431091578284
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• The Role of Natural Language Understanding in Multimodal Video-Based Dengue Diagnosis [1.5560241758540234]
  A vision-language framework is proposed to classify mosquito flight frames of uninfected and Dengue virus serotype 2 (DENV2)-infected mosquitoes.<n>The proposed method achieved 98.54% accuracy and 99.91% sensitivity at the frame level.<n>These findings suggest that vision-language models can provide a useful framework for analyzing infection-related biological behaviors from video data.
  arXiv  Detail & Related papers  (2026-08-13T00:27:22Z)
• Transformer-Based Wildlife Species Classification from Daily Movement Trajectories [13.570863502498336]
  We train sequence models on large-scale, 7-species GPS trajectories.<n>Trajectories models are evaluated using a protocol in which entire telemetry studies or regions are heldout during testing.<n>Transformers consistently achieve higher balanced accuracy with gains of approximately 8 to 22 percentage points.
  arXiv  Detail & Related papers  (2026-05-07T11:20:55Z)
• Less Is More: An Explainable AI Framework for Lightweight Malaria Classification [0.0]
  This study addresses whether complex neural networks are essential for the simple binary classification task of malaria.<n>We introduce the Extracted Morphological Feature Engineered (EMFE) pipeline, a transparent, reproducible, and low compute machine learning approach.
  arXiv  Detail & Related papers  (2025-11-22T14:46:59Z)
• Detection of retinal diseases using an accelerated reused convolutional network [3.5808917363708743]
  We create a new general model that incorporates our novel convolutional layer named ArConv layers.<n>The model has suitable complexity for use in mobile phones and can perform the task of diagnosing the presence of disease with high accuracy.
  arXiv  Detail & Related papers  (2025-10-05T14:44:09Z)
• Comparative Performance Analysis of Transformer-Based Pre-Trained Models for Detecting Keratoconus Disease [0.0]
  This study compares eight pre-trained CNNs for diagnosing keratoconus, a degenerative eye disease. MobileNetV2 was the best accurate model in identifying keratoconus and normal cases with few misclassifications.
  arXiv  Detail & Related papers  (2024-08-16T20:15:24Z)
• DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detection [52.74152717667157]
  We propose a lightweight module called Dual Attention Module (DAM) for capturing cross-dimension interaction relationships in-temporal skeletal data. It employs the frame attention mechanism to identify the most significant frames and the skeleton attention mechanism to capture broader relationships across fixed partitions with minimal parameters and flops.
  arXiv  Detail & Related papers  (2024-06-05T06:18:03Z)
• Matching the Neuronal Representations of V1 is Necessary to Improve Robustness in CNNs with V1-like Front-ends [1.8434042562191815]
  Recently, it was shown that simulating computations in early visual areas at the front of convolutional neural networks leads to improvements in robustness to image corruptions. Here, we show that the neuronal representations that emerge from precisely matching the distribution of RF properties found in primate V1 is key for this improvement in robustness.
  arXiv  Detail & Related papers  (2023-10-16T16:52:15Z)
• Domain Adaptive Synapse Detection with Weak Point Annotations [63.97144211520869]
  We present AdaSyn, a framework for domain adaptive synapse detection with weak point annotations. In the WASPSYN challenge at I SBI 2023, our method ranks the 1st place.
  arXiv  Detail & Related papers  (2023-08-31T05:05:53Z)
• Continuous time recurrent neural networks: overview and application to forecasting blood glucose in the intensive care unit [56.801856519460465]
  Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations. We demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting.
  arXiv  Detail & Related papers  (2023-04-14T09:39:06Z)
• Firearm Detection via Convolutional Neural Networks: Comparing a Semantic Segmentation Model Against End-to-End Solutions [68.8204255655161]
  Threat detection of weapons and aggressive behavior from live video can be used for rapid detection and prevention of potentially deadly incidents. One way for achieving this is through the use of artificial intelligence and, in particular, machine learning for image analysis. We compare a traditional monolithic end-to-end deep learning model and a previously proposed model based on an ensemble of simpler neural networks detecting fire-weapons via semantic segmentation.
  arXiv  Detail & Related papers  (2020-12-17T15:19:29Z)
• Robust Finite Mixture Regression for Heterogeneous Targets [70.19798470463378]
  We propose an FMR model that finds sample clusters and jointly models multiple incomplete mixed-type targets simultaneously. We provide non-asymptotic oracle performance bounds for our model under a high-dimensional learning framework. The results show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-10-12T03:27:07Z)
• A Generative Learning Approach for Spatio-temporal Modeling in Connected Vehicular Network [55.852401381113786]
  This paper proposes LaMI (Latency Model Inpainting), a novel framework to generate a comprehensive-temporal quality framework for wireless access latency of connected vehicles. LaMI adopts the idea from image inpainting and synthesizing and can reconstruct the missing latency samples by a two-step procedure. In particular, it first discovers the spatial correlation between samples collected in various regions using a patching-based approach and then feeds the original and highly correlated samples into a Varienational Autocoder (VAE)
  arXiv  Detail & Related papers  (2020-03-16T03:43:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.