Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany.
Institute for Computational Biology, University of Hamburg, Hamburg, Germany.
Parasit Vectors. 2024 Sep 2;17(1):372. doi: 10.1186/s13071-024-06459-3.
Mosquito-borne diseases are a major global health threat. Traditional morphological or molecular methods for identifying mosquito species often require specialized expertise or expensive laboratory equipment. The use of convolutional neural networks (CNNs) to identify mosquito species based on images may offer a promising alternative, but their practical implementation often remains limited. This study explores the applicability of CNNs in classifying mosquito species. It compares the efficacy of body and wing depictions across three image collection methods: a smartphone, macro-lens attached to a smartphone and a professional stereomicroscope. The study included 796 specimens of four morphologically similar Aedes species, Aedes aegypti, Ae. albopictus, Ae. koreicus and Ae. japonicus japonicus. The findings of this study indicate that CNN models demonstrate superior performance in wing-based classification 87.6% (95% CI: 84.2-91.0) compared to body-based classification 78.9% (95% CI: 77.7-80.0). Nevertheless, there are notable limitations of CNNs as they perform reliably across multiple devices only when trained specifically on those devices, resulting in an average decline of mean accuracy by 14%, even with extensive image augmentation. Additionally, we also estimate the required training data volume for effective classification, noting a reduced requirement for wing-based classification compared to body-based methods. Our study underscores the viability of both body and wing classification methods for mosquito species identification while emphasizing the need to address practical constraints in developing accessible classification systems.
蚊媒传染病是全球主要的健康威胁之一。传统的形态学或分子方法来鉴定蚊子种类通常需要专业知识或昂贵的实验室设备。利用卷积神经网络（CNN）根据图像来识别蚊子种类可能是一种很有前途的替代方法，但其实用性的实现往往仍然受到限制。本研究探讨了 CNN 在蚊子种类分类中的适用性。它比较了三种图像采集方法（智能手机、智能手机上附加的微距镜头和专业立体显微镜）中对蚊子身体和翅膀的描绘的效果。该研究包括 796 只形态相似的四种伊蚊物种（埃及伊蚊、白纹伊蚊、朝鲜伊蚊和日本按蚊）的标本。本研究的结果表明，与基于身体的分类 78.9%（95%置信区间：77.7-80.0）相比，CNN 模型在基于翅膀的分类中表现出更好的性能 87.6%（95%置信区间：84.2-91.0）。然而，CNN 也存在明显的局限性，只有在针对特定设备进行训练时，它们才能在多个设备上可靠地运行，这导致平均准确率下降了 14%，即使进行了广泛的图像扩充。此外，我们还估计了有效分类所需的训练数据量，注意到与基于身体的方法相比，基于翅膀的分类所需的数据量减少。我们的研究强调了身体和翅膀分类方法在蚊子种类鉴定中的可行性，同时强调了在开发可访问的分类系统时需要解决实际限制的重要性。
