Delimiting cryptic morphological variation among human malaria vector species using convolutional neural networks.
Delimiting cryptic morphological variation among human malaria vector species using convolutional neural networks.
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使用卷积神经网络划定人类疟疾载体物种之间的隐性形态变化。
DOI：
10.1371/journal.pntd.0008904
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发表时间：
2020-12
影响因子：
3.8
通讯作者：
Alvarez M
中科院分区：
文献类型：
--
作者：
Couret J;Moreira DC;Bernier D;Loberti AM;Dotson EM;Alvarez M
深度学习是区分图像类别的强大方法，人们越来越有兴趣将这些方法应用于划分物种，特别是在识别蚊子载体方面。蚊子种类的视觉识别是蚊媒疾病监测和管理的基础，但可能会受到蚊子媒介物种复合体（如传播疟疾的冈比亚按蚊复合体）的隐蔽形态变异的阻碍。我们试图将卷积神经网络（CNN）应用于蚊子的图像，作为概念验证，以确定使用蚊子的全身2D图像自动分类蚊子性别，属，种和品系的可行性。我们引入了一个包含1709张成年蚊子图片的文库，这些图片来自于5个地理区域的16个蚊媒物种和品系，其中4个神秘物种即使是训练有素的医学昆虫学家也不容易在形态上区分。我们提出了一种用于图像处理，数据增强以及CNN训练和验证的方法。我们最好的CNN配置实现了物种识别的96.96%和性别的98.48%的高预测准确率。我们的研究结果表明，CNN可以界定具有隐蔽形态变异的物种，单个物种的2个菌株，以及使用两种不同方法储存的单个菌落的标本。我们提出了CNN特征空间的可视化和预测，以解释我们的结果，我们进一步讨论了我们的研究结果在疟疾蚊子监测中的未来应用。快速准确地识别传播人类病原体的蚊子是蚊媒疾病监测的重要组成部分。对于传播疟疾的蚊子来说，这种识别可能很困难，因为许多蚊子在形态上无法区分，包括冈比亚按蚊物种复合体中的蚊子。我们从疾病控制和预防中心的16个实验室种群中拍摄了1709只蚊子，以创建一个蚊子全身图像数据库。我们提出了一种用于图像处理，数据增强以及卷积神经网络（CNN）的训练和验证的方法。我们将此方法应用于我们的蚊子图像数据库，发现类识别的预测准确率为96.96%，性别为98.48%。此外，我们的最佳模型准确地预测了同一物种的2种菌株之间的图像以及同一种群蚊子的2种储存方法之间的图像。这些结果表明，使用深度学习的图像分类可以成为识别疟疾蚊子的有用方法，即使是在具有隐蔽形态变异的物种之间。我们讨论了深度学习在疟疾蚊子监测中识别蚊子的应用。
Deep learning is a powerful approach for distinguishing classes of images, and there is a growing interest in applying these methods to delimit species, particularly in the identification of mosquito vectors. Visual identification of mosquito species is the foundation of mosquito-borne disease surveillance and management, but can be hindered by cryptic morphological variation in mosquito vector species complexes such as the malaria-transmitting Anopheles gambiae complex. We sought to apply Convolutional Neural Networks (CNNs) to images of mosquitoes as a proof-of-concept to determine the feasibility of automatic classification of mosquito sex, genus, species, and strains using whole-body, 2D images of mosquitoes. We introduce a library of 1, 709 images of adult mosquitoes collected from 16 colonies of mosquito vector species and strains originating from five geographic regions, with 4 cryptic species not readily distinguishable morphologically even by trained medical entomologists. We present a methodology for image processing, data augmentation, and training and validation of a CNN. Our best CNN configuration achieved high prediction accuracies of 96.96% for species identification and 98.48% for sex. Our results demonstrate that CNNs can delimit species with cryptic morphological variation, 2 strains of a single species, and specimens from a single colony stored using two different methods. We present visualizations of the CNN feature space and predictions for interpretation of our results, and we further discuss applications of our findings for future applications in malaria mosquito surveillance. Rapid and accurate identification of mosquitoes that transmit human pathogens is an essential part of mosquito-borne disease surveillance. Such identification can be difficult for mosquitoes that transmit malaria, as many are morphologically indistinguishable, including those in the Anopheles gambiae species complex. We photographed 1, 709 individual mosquitoes from 16 laboratory colonies housed at the Centers for Disease Control and Prevention to create a database of whole-body mosquito images. We present a methodology for image processing, data augmentation, and training and validation of a convolutional neural network (CNN). We applied this method to our mosquito image database, finding a 96.96% prediction accuracy for class identification and 98.48% for sex. Further, our best model accurately predicted images between 2 strains of a single species and between 2 storage methods of mosquitoes from the same colony. These results demonstrate that image classification with deep learning can be a useful method for malaria mosquito identification, even among species with cryptic morphological variation. We discuss the application of deep learning to mosquito identification in malaria mosquito surveillance.
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3
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