Manual identification of foraminifera species or morphotypes under stereoscopic microscopes is time-consuming for the taxonomist, and a long-time goal has been automating this process to improve efficiency and repeatability. Recent advances in computation hardware have seen deep convolutional neural networks emerge as the state-of-the-art technique for image-based automated classification. Here, we describe a method for classifying large down-core foraminifera image set using convolutional neural networks. Construction of the classifier is demonstrated on the publically available endless forams image set with an best accuracy of approximately 90%. A complete down-core analysis is performed for benthic species in the Holocene period for core MD02-2518 from the North Eastern Pacific, and the relative abundances compare favourably with manual counting, showing the same signal dynamics. Using our workflow opens the way to automated paleo-reconstruction based on computer image analysis, and can be employed using our labelling and classification software ParticleTrieur.

中文翻译：

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使用卷积神经网络对核心有孔虫图像集进行分类

对于分类学家而言，在立体显微镜下手动识别有孔虫种类或形态是很费时的，长期目标是使这一过程自动化以提高效率和可重复性。计算硬件的最新进展已将深度卷积神经网络作为基于图像的自动分类的最新技术而出现。在这里，我们描述了一种使用卷积神经网络对大型有核有孔虫图像集进行分类的方法。在公开的无休止的论坛中展示了分类器的构造图像集的最佳准确性约为90％。对来自东北太平洋的MD02-2518岩心在全新世时期的底栖生物进行了完整的下核分析，相对丰度与人工计数相比具有优势，显示了相同的信号动态。使用我们的工作流程为基于计算机图像分析的自动化古重建开辟了道路，并且可以使用我们的标记和分类软件ParticleTrieur进行使用。