FN Archimer Export Format
PT J
TI Automated analysis of foraminifera fossil records by image classification using a convolutional neural network
BT 
AF Marchant, Ross
   Tetard, Martin
   Pratiwi, Adnya
   Adebayo, Michael
   de Garidel-Thoron, Thibault
AS 1:1,2;2:1;3:1;4:1;5:1;
FF 1:;2:;3:;4:;5:;
C1 Aix-Marseille Université, CNRS, IRD, Coll. De France, INRAE, CEREGE, Technopôle de l'Arbois-Méditerranée, Aix-en-Provence, 13545, France
   School of Electrical Engineering & Robotics, Queensland University of Technology, Brisbane, Australia
C2 UNIV AIX MARSEILLE, FRANCE
   UNIV QUEENSLAND, AUSTRALIA
IN DOAJ
IF 0.5
TC 28
UR https://archimer.ifremer.fr/doc/00655/76687/77826.pdf
LA English
DT Article
CR MD 126 / MONA
BO Marion Dufresne
AB Manual identification of foraminiferal morphospecies or morphotypes under stereo microscopes is time consuming for micropalaeontologists and not possible for nonspecialists. Therefore, a long-term goal has been to automate this process to improve its 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 foraminifera image sets using convolutional neural networks. Construction of the classifier is demonstrated on the publicly available Endless Forams image set with a best accuracy of approximately 90 %. A complete automatic analysis is performed for benthic species dated to the last deglacial period for a sediment core from the north-eastern Pacific and for planktonic species dated from the present until 180 000 years ago in a core from the western Pacific warm pool. The relative abundances from automatic counting based on more than 500 000 images compare favourably with manual counting, showing the same signal dynamics. Our workflow opens the way to automated palaeoceanographic reconstruction based on computer image analysis and is freely available for use. 
PY 2020
PD OCT
SO Journal Of Micropalaeontology
SN 0262-821X
PU Copernicus GmbH
VL 39
IS 2
UT 000582340200001
BP 183
EP 202
DI 10.5194/jm-39-183-2020
ID 76687
ER

EF