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.
Automated classification of hip fractures using deep convolutional neural networks with orthopedic surgeon-level accuracy: ensemble decision-making with antero-posterior and lateral radiographs
