Karim Ahmed Akib Jawad, Mahmud Muhammad Zawad, Khan Riasat
Electrical and Computer Engineering, North South University, Dhaka, Bangladesh.
PLoS Comput Biol. 2024 Dec 13;20(12):e1012654. doi: 10.1371/journal.pcbi.1012654. eCollection 2024 Dec.
Mosquito-related diseases pose a significant threat to global public health, necessitating efficient and accurate mosquito classification for effective surveillance and control. This work presents an innovative approach to mosquito classification by leveraging state-of-the-art vision transformers and open-set learning techniques. A novel framework has been introduced that integrates Transformer-based deep learning models with comprehensive data augmentation and preprocessing methods, enabling robust and precise identification of ten mosquito species. The Swin Transformer model achieves the best performance for traditional closed-set learning with 99.60% accuracy and 0.996 F1 score. The lightweight MobileViT technique attains an almost equivalent accuracy of 98.90% with significantly reduced parameters and model complexities. Next, the applied deep learning models' adaptability and generalizability in a static environment have been enhanced by using new classes of data samples during the inference stage that have not been included in the training set. The proposed framework's ability to handle unseen classes like insects similar to mosquitoes, even humans, through open-set learning further enhances its practical applicability employing the OpenMax technique and Weibull distribution. The traditional CNN model, Xception, outperforms the latest transformer with higher accuracy and F1 score for open-set learning. The study's findings highlight the transformative potential of advanced deep-learning architectures in entomology, providing a strong groundwork for future research and development in mosquito surveillance and vector control. The implications of this work extend beyond mosquito classification, offering valuable insights for broader ecological and environmental monitoring applications.
与蚊子相关的疾病对全球公共卫生构成重大威胁，因此需要进行高效准确的蚊子分类，以实现有效的监测和控制。这项工作提出了一种创新方法，通过利用先进的视觉Transformer和开放集学习技术对蚊子进行分类。引入了一个新颖的框架，该框架将基于Transformer的深度学习模型与全面的数据增强和预处理方法相结合，能够对十种蚊子进行强大而精确的识别。Swin Transformer模型在传统的封闭集学习中表现最佳，准确率为99.60%，F1分数为0.996。轻量级的MobileViT技术在参数和模型复杂度显著降低的情况下，达到了几乎相同的98.90%的准确率。接下来，通过在推理阶段使用未包含在训练集中的新类数据样本，增强了应用的深度学习模型在静态环境中的适应性和通用性。所提出的框架通过开放集学习，利用OpenMax技术和威布尔分布，能够处理类似蚊子的昆虫甚至人类等未见类别，进一步提高了其实用适用性。传统的CNN模型Xception在开放集学习中以更高的准确率和F1分数优于最新的Transformer。该研究结果突出了先进深度学习架构在昆虫学中的变革潜力，为蚊子监测和病媒控制的未来研究与开发奠定了坚实基础。这项工作的影响不仅限于蚊子分类，还为更广泛的生态和环境监测应用提供了有价值的见解。
